{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase 3 Weighted Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zake7\\Anaconda3\\lib\\site-packages\\gensim\\utils.py:1197: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n",
      "Using TensorFlow backend.\n",
      "C:\\Users\\zake7\\Anaconda3\\lib\\site-packages\\fuzzywuzzy\\fuzz.py:35: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n",
      "  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "\n",
    "import os\n",
    "import re\n",
    "import csv\n",
    "import codecs\n",
    "import gensim\n",
    "import itertools\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import operator\n",
    "import sys\n",
    "\n",
    "from nltk import ngrams\n",
    "from collections import Counter\n",
    "from string import punctuation\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "from iwillwin.trainer.supervised_trainer import KerasModelTrainer\n",
    "from iwillwin.data_utils.data_helpers import DataTransformer, DataLoader\n",
    "from iwillwin.config import dataset_config\n",
    "from iwillwin.data_utils.feature_engineering import FeatureCreator\n",
    "\n",
    "from fuzzywuzzy import fuzz\n",
    "from nltk.corpus import stopwords\n",
    "from tqdm import tqdm\n",
    "from scipy.stats import skew, kurtosis\n",
    "from scipy.spatial.distance import cosine, cityblock, jaccard, canberra, euclidean, minkowski, braycurtis\n",
    "from nltk import word_tokenize\n",
    "\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "import os\n",
    "import re\n",
    "import csv\n",
    "import codecs\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import operator\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "\n",
    "########################################\n",
    "## import packages\n",
    "########################################\n",
    "import os\n",
    "import re\n",
    "import csv\n",
    "import codecs\n",
    "import numpy as np\n",
    "np.random.seed(1337)\n",
    "\n",
    "import pandas as pd\n",
    "import operator\n",
    "import sys\n",
    "\n",
    "from string import punctuation\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "from iwillwin.trainer.supervised_trainer import KerasModelTrainer\n",
    "from iwillwin.data_utils.data_helpers import DataTransformer, DataLoader\n",
    "from iwillwin.config import dataset_config\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\zake7\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.466 seconds.\n",
      "Prefix dict has been built succesfully.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[DataHelper] Apply normalization on value-type columns\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zake7\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doing preprocessing...\n",
      "Transforming words to indices...\n",
      "Shape of data tensor: (320552, 50) (320552, 50)\n",
      "Shape of label tensor: (320552,)\n",
      "Preprocessed.\n",
      "Number of unique words 83265\n"
     ]
    }
   ],
   "source": [
    "NB_WORDS, MAX_SEQUENCE_LENGTH = 50000, 50\n",
    "data_transformer = DataTransformer(max_num_words=NB_WORDS, max_sequence_length=MAX_SEQUENCE_LENGTH, char_level=False,\n",
    "                                   normalization=True, features_processed=True)\n",
    "trains_nns, tests_nns, labels = data_transformer.prepare_data(dual=False)\n",
    "print(\"Number of unique words\", len(data_transformer.tokenizer.index_docs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trains_meta = trains_nns[2]\n",
    "tests_meta = tests_nns[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('../data/dataset/train.csv')\n",
    "test_df = pd.read_csv('../data/dataset/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan <class 'float'>\n",
      "nan <class 'float'>\n",
      "nan <class 'float'>\n",
      "nan <class 'float'>\n",
      "nan <class 'float'>\n",
      "nan <class 'float'>\n",
      "nan <class 'float'>\n",
      "nan <class 'float'>\n"
     ]
    }
   ],
   "source": [
    "rumor_words = ['辟谣', '谣言', '勿传', '假的']\n",
    "\n",
    "def is_rumor(text):\n",
    "    if type(text) != str:\n",
    "        print(text, type(text))\n",
    "        return 0\n",
    "    for rumor_word in rumor_words:\n",
    "        if rumor_word in text:\n",
    "            return 1\n",
    "    return 0\n",
    "\n",
    "def has_split_symbol(text):\n",
    "    if type(text) != str:\n",
    "        return 0\n",
    "    if '|' in text:\n",
    "        return 1\n",
    "    return 0\n",
    "\n",
    "for df in [train_df, test_df]:\n",
    "    df['has_|'] = df['title2_zh'].apply(has_split_symbol)\n",
    "    df['has_rumor_words'] = df['title2_zh'].apply(is_rumor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_has_rumor = train_df.has_rumor_words.values\n",
    "test_has_rumor = test_df.has_rumor_words.values\n",
    "\n",
    "trick_trains_features = np.concatenate((trains_nns[2], train_has_rumor.reshape((-1, 1))), axis=1)\n",
    "trick_tests_features = np.concatenate((tests_nns[2], test_has_rumor.reshape((-1, 1))), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "oof_file_names = sorted([f for f in listdir('../data/oofs/') if isfile(join('../data/oofs/', f)) and f != '.gitkeep'])\n",
    "preds_file_names = [name.replace('-Train', '') for name in oof_file_names]\n",
    "\n",
    "oofs = []\n",
    "preds = []\n",
    "for name in oof_file_names:\n",
    "    oofs.append(pd.read_csv('../data/oofs/' + name))\n",
    "for name in preds_file_names:\n",
    "    preds.append(pd.read_csv('../data/output/' + name))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 3Embedding-3LayersDenseCNN42-NoDrop-NoClassWeighted-NoEM-Train-L0.809633-NB5000.csv\n",
      "1 3Embedding-3LayersDenseRNN42-Drop01-NoMeta-NoClassWeighted-WithEM-Train-L0.816583-NB5000.csv\n",
      "2 3Embedding-ESIM-Drop01-NoMeta-NoClassWeighted-NoEM-Train-L0.833565-NB5000.csv\n",
      "3 WordSGNS-DenseCNN5Layers-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L0.838202-NB100000.csv\n",
      "4 WordSGNS-ESIM-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L1.104962-NB100000.csv\n",
      "5 WordTC-DenseCNN5Layers-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L0.8440-NB100000.csv\n",
      "6 WordTC-DenseRNN-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L0.854586-NB100000.csv\n",
      "7 WordTC-ESIM-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L0.374334-NB100000.csv\n",
      "8 WordTC-Gated4GWindows-NoMeta-3P-NoEM-NoClassWeighted-3Layers-withEM-Train-L0.836860-NB100000.csv\n"
     ]
    }
   ],
   "source": [
    "for i, name in enumerate(oof_file_names):\n",
    "    print(i, name)\n",
    "    \n",
    "trains = pd.DataFrame()\n",
    "tests = pd.DataFrame()\n",
    "\n",
    "for i in range(len(oof_file_names)):\n",
    "    for label_type in ['agreed', 'disagreed', 'unrelated']:\n",
    "        trains['oofs_{}_{}'.format(i, label_type)] = oofs[i][label_type].values\n",
    "        tests['oofs_pred{}_{}'.format(i, label_type)] = preds[i][label_type].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['3Embedding-3LayersDenseCNN42-NoDrop-NoClassWeighted-NoEM-Train-L0.809633-NB5000.csv',\n",
       " '3Embedding-3LayersDenseRNN42-Drop01-NoMeta-NoClassWeighted-WithEM-Train-L0.816583-NB5000.csv',\n",
       " '3Embedding-ESIM-Drop01-NoMeta-NoClassWeighted-NoEM-Train-L0.833565-NB5000.csv',\n",
       " 'WordSGNS-DenseCNN5Layers-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L0.838202-NB100000.csv',\n",
       " 'WordSGNS-ESIM-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L1.104962-NB100000.csv',\n",
       " 'WordTC-DenseCNN5Layers-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L0.8440-NB100000.csv',\n",
       " 'WordTC-DenseRNN-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L0.854586-NB100000.csv',\n",
       " 'WordTC-ESIM-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L0.374334-NB100000.csv',\n",
       " 'WordTC-Gated4GWindows-NoMeta-3P-NoEM-NoClassWeighted-3Layers-withEM-Train-L0.836860-NB100000.csv']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oof_file_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unrelated = pd.DataFrame()\n",
    "agreeds = pd.DataFrame()\n",
    "disagreeds = pd.DataFrame()\n",
    "\n",
    "#check_oofs = True\n",
    "check_oofs = False\n",
    "\n",
    "\n",
    "if check_oofs:\n",
    "    for i, oof in enumerate(oofs):\n",
    "        agreeds['oofs_agreed_{}'.format(i)] = oofs[i]['agreed'].values\n",
    "        unrelated['oofs_unrelated_{}'.format(i)] = oofs[i]['unrelated'].values\n",
    "        disagreeds['oofs_disagreeds_{}'.format(i)] = oofs[i]['disagreed'].values\n",
    "else:\n",
    "    for i, oof in enumerate(oofs):\n",
    "        agreeds['oofs_agreed_{}'.format(i)] = preds[i]['agreed'].values\n",
    "        unrelated['oofs_unrelated_{}'.format(i)] = preds[i]['unrelated'].values\n",
    "        disagreeds['oofs_disagreeds_{}'.format(i)] = preds[i]['disagreed'].values  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oofs_agreed_0</th>\n",
       "      <th>oofs_agreed_1</th>\n",
       "      <th>oofs_agreed_2</th>\n",
       "      <th>oofs_agreed_3</th>\n",
       "      <th>oofs_agreed_4</th>\n",
       "      <th>oofs_agreed_5</th>\n",
       "      <th>oofs_agreed_6</th>\n",
       "      <th>oofs_agreed_7</th>\n",
       "      <th>oofs_agreed_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.982310</td>\n",
       "      <td>0.976632</td>\n",
       "      <td>0.959797</td>\n",
       "      <td>0.956807</td>\n",
       "      <td>0.961449</td>\n",
       "      <td>0.961597</td>\n",
       "      <td>0.959404</td>\n",
       "      <td>0.962629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_1</th>\n",
       "      <td>0.982310</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.982479</td>\n",
       "      <td>0.958966</td>\n",
       "      <td>0.957473</td>\n",
       "      <td>0.961494</td>\n",
       "      <td>0.961780</td>\n",
       "      <td>0.961639</td>\n",
       "      <td>0.961288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_2</th>\n",
       "      <td>0.976632</td>\n",
       "      <td>0.982479</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.955476</td>\n",
       "      <td>0.959167</td>\n",
       "      <td>0.957512</td>\n",
       "      <td>0.961050</td>\n",
       "      <td>0.963775</td>\n",
       "      <td>0.956841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_3</th>\n",
       "      <td>0.959797</td>\n",
       "      <td>0.958966</td>\n",
       "      <td>0.955476</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.982100</td>\n",
       "      <td>0.977313</td>\n",
       "      <td>0.970090</td>\n",
       "      <td>0.972992</td>\n",
       "      <td>0.973543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_4</th>\n",
       "      <td>0.956807</td>\n",
       "      <td>0.957473</td>\n",
       "      <td>0.959167</td>\n",
       "      <td>0.982100</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.971693</td>\n",
       "      <td>0.972289</td>\n",
       "      <td>0.979278</td>\n",
       "      <td>0.968705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_5</th>\n",
       "      <td>0.961449</td>\n",
       "      <td>0.961494</td>\n",
       "      <td>0.957512</td>\n",
       "      <td>0.977313</td>\n",
       "      <td>0.971693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.981701</td>\n",
       "      <td>0.981716</td>\n",
       "      <td>0.982047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_6</th>\n",
       "      <td>0.961597</td>\n",
       "      <td>0.961780</td>\n",
       "      <td>0.961050</td>\n",
       "      <td>0.970090</td>\n",
       "      <td>0.972289</td>\n",
       "      <td>0.981701</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.983917</td>\n",
       "      <td>0.982819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_7</th>\n",
       "      <td>0.959404</td>\n",
       "      <td>0.961639</td>\n",
       "      <td>0.963775</td>\n",
       "      <td>0.972992</td>\n",
       "      <td>0.979278</td>\n",
       "      <td>0.981716</td>\n",
       "      <td>0.983917</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_8</th>\n",
       "      <td>0.962629</td>\n",
       "      <td>0.961288</td>\n",
       "      <td>0.956841</td>\n",
       "      <td>0.973543</td>\n",
       "      <td>0.968705</td>\n",
       "      <td>0.982047</td>\n",
       "      <td>0.982819</td>\n",
       "      <td>0.978351</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               oofs_agreed_0  oofs_agreed_1  oofs_agreed_2  oofs_agreed_3  \\\n",
       "oofs_agreed_0       1.000000       0.982310       0.976632       0.959797   \n",
       "oofs_agreed_1       0.982310       1.000000       0.982479       0.958966   \n",
       "oofs_agreed_2       0.976632       0.982479       1.000000       0.955476   \n",
       "oofs_agreed_3       0.959797       0.958966       0.955476       1.000000   \n",
       "oofs_agreed_4       0.956807       0.957473       0.959167       0.982100   \n",
       "oofs_agreed_5       0.961449       0.961494       0.957512       0.977313   \n",
       "oofs_agreed_6       0.961597       0.961780       0.961050       0.970090   \n",
       "oofs_agreed_7       0.959404       0.961639       0.963775       0.972992   \n",
       "oofs_agreed_8       0.962629       0.961288       0.956841       0.973543   \n",
       "\n",
       "               oofs_agreed_4  oofs_agreed_5  oofs_agreed_6  oofs_agreed_7  \\\n",
       "oofs_agreed_0       0.956807       0.961449       0.961597       0.959404   \n",
       "oofs_agreed_1       0.957473       0.961494       0.961780       0.961639   \n",
       "oofs_agreed_2       0.959167       0.957512       0.961050       0.963775   \n",
       "oofs_agreed_3       0.982100       0.977313       0.970090       0.972992   \n",
       "oofs_agreed_4       1.000000       0.971693       0.972289       0.979278   \n",
       "oofs_agreed_5       0.971693       1.000000       0.981701       0.981716   \n",
       "oofs_agreed_6       0.972289       0.981701       1.000000       0.983917   \n",
       "oofs_agreed_7       0.979278       0.981716       0.983917       1.000000   \n",
       "oofs_agreed_8       0.968705       0.982047       0.982819       0.978351   \n",
       "\n",
       "               oofs_agreed_8  \n",
       "oofs_agreed_0       0.962629  \n",
       "oofs_agreed_1       0.961288  \n",
       "oofs_agreed_2       0.956841  \n",
       "oofs_agreed_3       0.973543  \n",
       "oofs_agreed_4       0.968705  \n",
       "oofs_agreed_5       0.982047  \n",
       "oofs_agreed_6       0.982819  \n",
       "oofs_agreed_7       0.978351  \n",
       "oofs_agreed_8       1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agreeds.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oofs_disagreeds_0</th>\n",
       "      <th>oofs_disagreeds_1</th>\n",
       "      <th>oofs_disagreeds_2</th>\n",
       "      <th>oofs_disagreeds_3</th>\n",
       "      <th>oofs_disagreeds_4</th>\n",
       "      <th>oofs_disagreeds_5</th>\n",
       "      <th>oofs_disagreeds_6</th>\n",
       "      <th>oofs_disagreeds_7</th>\n",
       "      <th>oofs_disagreeds_8</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>oofs_disagreeds_0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.954897</td>\n",
       "      <td>0.951416</td>\n",
       "      <td>0.912412</td>\n",
       "      <td>0.913741</td>\n",
       "      <td>0.912573</td>\n",
       "      <td>0.916230</td>\n",
       "      <td>0.912694</td>\n",
       "      <td>0.908535</td>\n",
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       "    <tr>\n",
       "      <th>oofs_disagreeds_1</th>\n",
       "      <td>0.954897</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.964977</td>\n",
       "      <td>0.920113</td>\n",
       "      <td>0.918646</td>\n",
       "      <td>0.920680</td>\n",
       "      <td>0.921862</td>\n",
       "      <td>0.920311</td>\n",
       "      <td>0.911443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_disagreeds_2</th>\n",
       "      <td>0.951416</td>\n",
       "      <td>0.964977</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.919030</td>\n",
       "      <td>0.925045</td>\n",
       "      <td>0.915579</td>\n",
       "      <td>0.923305</td>\n",
       "      <td>0.926555</td>\n",
       "      <td>0.913171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_disagreeds_3</th>\n",
       "      <td>0.912412</td>\n",
       "      <td>0.920113</td>\n",
       "      <td>0.919030</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.967334</td>\n",
       "      <td>0.956456</td>\n",
       "      <td>0.945988</td>\n",
       "      <td>0.954167</td>\n",
       "      <td>0.943892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_disagreeds_4</th>\n",
       "      <td>0.913741</td>\n",
       "      <td>0.918646</td>\n",
       "      <td>0.925045</td>\n",
       "      <td>0.967334</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.946770</td>\n",
       "      <td>0.944932</td>\n",
       "      <td>0.961943</td>\n",
       "      <td>0.939010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_disagreeds_5</th>\n",
       "      <td>0.912573</td>\n",
       "      <td>0.920680</td>\n",
       "      <td>0.915579</td>\n",
       "      <td>0.956456</td>\n",
       "      <td>0.946770</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.965450</td>\n",
       "      <td>0.960278</td>\n",
       "      <td>0.955281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_disagreeds_6</th>\n",
       "      <td>0.916230</td>\n",
       "      <td>0.921862</td>\n",
       "      <td>0.923305</td>\n",
       "      <td>0.945988</td>\n",
       "      <td>0.944932</td>\n",
       "      <td>0.965450</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.963620</td>\n",
       "      <td>0.958197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_disagreeds_7</th>\n",
       "      <td>0.912694</td>\n",
       "      <td>0.920311</td>\n",
       "      <td>0.926555</td>\n",
       "      <td>0.954167</td>\n",
       "      <td>0.961943</td>\n",
       "      <td>0.960278</td>\n",
       "      <td>0.963620</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.954127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_disagreeds_8</th>\n",
       "      <td>0.908535</td>\n",
       "      <td>0.911443</td>\n",
       "      <td>0.913171</td>\n",
       "      <td>0.943892</td>\n",
       "      <td>0.939010</td>\n",
       "      <td>0.955281</td>\n",
       "      <td>0.958197</td>\n",
       "      <td>0.954127</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   oofs_disagreeds_0  oofs_disagreeds_1  oofs_disagreeds_2  \\\n",
       "oofs_disagreeds_0           1.000000           0.954897           0.951416   \n",
       "oofs_disagreeds_1           0.954897           1.000000           0.964977   \n",
       "oofs_disagreeds_2           0.951416           0.964977           1.000000   \n",
       "oofs_disagreeds_3           0.912412           0.920113           0.919030   \n",
       "oofs_disagreeds_4           0.913741           0.918646           0.925045   \n",
       "oofs_disagreeds_5           0.912573           0.920680           0.915579   \n",
       "oofs_disagreeds_6           0.916230           0.921862           0.923305   \n",
       "oofs_disagreeds_7           0.912694           0.920311           0.926555   \n",
       "oofs_disagreeds_8           0.908535           0.911443           0.913171   \n",
       "\n",
       "                   oofs_disagreeds_3  oofs_disagreeds_4  oofs_disagreeds_5  \\\n",
       "oofs_disagreeds_0           0.912412           0.913741           0.912573   \n",
       "oofs_disagreeds_1           0.920113           0.918646           0.920680   \n",
       "oofs_disagreeds_2           0.919030           0.925045           0.915579   \n",
       "oofs_disagreeds_3           1.000000           0.967334           0.956456   \n",
       "oofs_disagreeds_4           0.967334           1.000000           0.946770   \n",
       "oofs_disagreeds_5           0.956456           0.946770           1.000000   \n",
       "oofs_disagreeds_6           0.945988           0.944932           0.965450   \n",
       "oofs_disagreeds_7           0.954167           0.961943           0.960278   \n",
       "oofs_disagreeds_8           0.943892           0.939010           0.955281   \n",
       "\n",
       "                   oofs_disagreeds_6  oofs_disagreeds_7  oofs_disagreeds_8  \n",
       "oofs_disagreeds_0           0.916230           0.912694           0.908535  \n",
       "oofs_disagreeds_1           0.921862           0.920311           0.911443  \n",
       "oofs_disagreeds_2           0.923305           0.926555           0.913171  \n",
       "oofs_disagreeds_3           0.945988           0.954167           0.943892  \n",
       "oofs_disagreeds_4           0.944932           0.961943           0.939010  \n",
       "oofs_disagreeds_5           0.965450           0.960278           0.955281  \n",
       "oofs_disagreeds_6           1.000000           0.963620           0.958197  \n",
       "oofs_disagreeds_7           0.963620           1.000000           0.954127  \n",
       "oofs_disagreeds_8           0.958197           0.954127           1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disagreeds.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>oofs_unrelated_0</th>\n",
       "      <th>oofs_unrelated_1</th>\n",
       "      <th>oofs_unrelated_2</th>\n",
       "      <th>oofs_unrelated_3</th>\n",
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       "      <th>oofs_unrelated_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978855</td>\n",
       "      <td>0.972572</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_1</th>\n",
       "      <td>0.978855</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.955456</td>\n",
       "      <td>0.954800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_2</th>\n",
       "      <td>0.972572</td>\n",
       "      <td>0.979417</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.957542</td>\n",
       "      <td>0.949502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_3</th>\n",
       "      <td>0.952930</td>\n",
       "      <td>0.952496</td>\n",
       "      <td>0.948158</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.979002</td>\n",
       "      <td>0.973876</td>\n",
       "      <td>0.965568</td>\n",
       "      <td>0.969264</td>\n",
       "      <td>0.969451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_4</th>\n",
       "      <td>0.949506</td>\n",
       "      <td>0.950368</td>\n",
       "      <td>0.952290</td>\n",
       "      <td>0.979002</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.967039</td>\n",
       "      <td>0.967506</td>\n",
       "      <td>0.975886</td>\n",
       "      <td>0.963249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_5</th>\n",
       "      <td>0.954771</td>\n",
       "      <td>0.955304</td>\n",
       "      <td>0.950251</td>\n",
       "      <td>0.973876</td>\n",
       "      <td>0.967039</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978773</td>\n",
       "      <td>0.978637</td>\n",
       "      <td>0.978786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_6</th>\n",
       "      <td>0.955016</td>\n",
       "      <td>0.955510</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.965568</td>\n",
       "      <td>0.967506</td>\n",
       "      <td>0.978773</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.981089</td>\n",
       "      <td>0.979624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_7</th>\n",
       "      <td>0.952586</td>\n",
       "      <td>0.955456</td>\n",
       "      <td>0.957542</td>\n",
       "      <td>0.969264</td>\n",
       "      <td>0.975886</td>\n",
       "      <td>0.978637</td>\n",
       "      <td>0.981089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_8</th>\n",
       "      <td>0.955786</td>\n",
       "      <td>0.954800</td>\n",
       "      <td>0.949502</td>\n",
       "      <td>0.969451</td>\n",
       "      <td>0.963249</td>\n",
       "      <td>0.978786</td>\n",
       "      <td>0.979624</td>\n",
       "      <td>0.974969</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  oofs_unrelated_0  oofs_unrelated_1  oofs_unrelated_2  \\\n",
       "oofs_unrelated_0          1.000000          0.978855          0.972572   \n",
       "oofs_unrelated_1          0.978855          1.000000          0.979417   \n",
       "oofs_unrelated_2          0.972572          0.979417          1.000000   \n",
       "oofs_unrelated_3          0.952930          0.952496          0.948158   \n",
       "oofs_unrelated_4          0.949506          0.950368          0.952290   \n",
       "oofs_unrelated_5          0.954771          0.955304          0.950251   \n",
       "oofs_unrelated_6          0.955016          0.955510          0.954365   \n",
       "oofs_unrelated_7          0.952586          0.955456          0.957542   \n",
       "oofs_unrelated_8          0.955786          0.954800          0.949502   \n",
       "\n",
       "                  oofs_unrelated_3  oofs_unrelated_4  oofs_unrelated_5  \\\n",
       "oofs_unrelated_0          0.952930          0.949506          0.954771   \n",
       "oofs_unrelated_1          0.952496          0.950368          0.955304   \n",
       "oofs_unrelated_2          0.948158          0.952290          0.950251   \n",
       "oofs_unrelated_3          1.000000          0.979002          0.973876   \n",
       "oofs_unrelated_4          0.979002          1.000000          0.967039   \n",
       "oofs_unrelated_5          0.973876          0.967039          1.000000   \n",
       "oofs_unrelated_6          0.965568          0.967506          0.978773   \n",
       "oofs_unrelated_7          0.969264          0.975886          0.978637   \n",
       "oofs_unrelated_8          0.969451          0.963249          0.978786   \n",
       "\n",
       "                  oofs_unrelated_6  oofs_unrelated_7  oofs_unrelated_8  \n",
       "oofs_unrelated_0          0.955016          0.952586          0.955786  \n",
       "oofs_unrelated_1          0.955510          0.955456          0.954800  \n",
       "oofs_unrelated_2          0.954365          0.957542          0.949502  \n",
       "oofs_unrelated_3          0.965568          0.969264          0.969451  \n",
       "oofs_unrelated_4          0.967506          0.975886          0.963249  \n",
       "oofs_unrelated_5          0.978773          0.978637          0.978786  \n",
       "oofs_unrelated_6          1.000000          0.981089          0.979624  \n",
       "oofs_unrelated_7          0.981089          1.000000          0.974969  \n",
       "oofs_unrelated_8          0.979624          0.974969          1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unrelated.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare Different Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# word level\n",
    "ensemble_trains = trains.values[:, :-9]\n",
    "ensemble_tests = tests.values[:, :-9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Only use oofs\n",
    "#ensemble_trains = trains.values\n",
    "#ensemble_tests = tests.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use oof and meta features\n",
    "#ensemble_trains = np.concatenate((trains.values, trains_meta), axis=1)\n",
    "#ensemble_tests = np.concatenate((tests.values, tests_meta), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# use oofs and meta-features\n",
    "#ensemble_trains = np.concatenate((trains.values, trick_trains_features), axis=1)\n",
    "#ensemble_tests = np.concatenate((tests.values, trick_tests_features), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#ensemble_trains = trick_trains_features\n",
    "#ensemble_tests = trick_tests_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fit_every_feature_model(feature_data, label, feature_test_data, fold_count=3, predict=True):\n",
    "    predictions = np.zeros(shape=[len(feature_test_data), 3])\n",
    "    fold_size = len(feature_data) // fold_count\n",
    "    oofs = []\n",
    "    \n",
    "    log_loss = 0\n",
    "    for fold_id in range(fold_count):\n",
    "        print(\"Fold : \", fold_id)\n",
    "        fold_start = fold_size * fold_id\n",
    "        fold_end = fold_start + fold_size\n",
    "        if fold_id == fold_count - 1:\n",
    "            fold_end = len(feature_data)\n",
    "                \n",
    "        train_x = np.concatenate([feature_data[:fold_start], feature_data[fold_end:]])\n",
    "        train_y = np.concatenate([label[:fold_start], label[fold_end:]])\n",
    "\n",
    "        val_x = feature_data[fold_start:fold_end]\n",
    "        val_y = label[fold_start:fold_end]\n",
    "        \n",
    "        lgb_train = lgb.Dataset(train_x, train_y)\n",
    "        lgb_val = lgb.Dataset(val_x, val_y)\n",
    "        \n",
    "        lgb_params = {\n",
    "            'boosting_type' : 'gbdt',\n",
    "            'objective' : 'multiclass',\n",
    "            'num_class':3,\n",
    "            'metric' : {'multi_logloss',},\n",
    "            'learning_rate' : 0.01,\n",
    "            'feature_fraction' : 0.8,\n",
    "            'bagging_fraction': 0.9,\n",
    "            'bagging_freq': 1,\n",
    "            'num_leaves' : 4,\n",
    "            'max_depth': 16,\n",
    "            'random_state': 42,\n",
    "            'nthread': 8,\n",
    "        }\n",
    "        \n",
    "        lgbm_model = lgb.train(lgb_params, lgb_train, num_boost_round=100000, valid_sets=[lgb_train, lgb_val],\n",
    "                        early_stopping_rounds=1000, verbose_eval=100)\n",
    "        \n",
    "        lgb.plot_importance(lgbm_model)\n",
    "        plt.show()\n",
    "        if predict:\n",
    "            prediction = lgbm_model.predict(feature_test_data, num_iteration=lgbm_model.best_iteration)\n",
    "            oof_prediction = lgbm_model.predict(val_x, num_iteration=lgbm_model.best_iteration)\n",
    "            score = metrics.log_loss(val_y, oof_prediction)\n",
    "            print(\"Fold\", fold_id, \"log loss\", score, \"in\", lgbm_model.best_iteration)\n",
    "            log_loss += score\n",
    "            oofs.append(oof_prediction)\n",
    "            predictions += prediction\n",
    "            del lgbm_model\n",
    "    predictions /= fold_count   \n",
    "    print(\"Training  Finish\")\n",
    "\n",
    "    return predictions, log_loss / fold_count, oofs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold :  0\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.533625\tvalid_1's multi_logloss: 0.536458\n",
      "[200]\ttraining's multi_logloss: 0.366426\tvalid_1's multi_logloss: 0.371247\n",
      "[300]\ttraining's multi_logloss: 0.30671\tvalid_1's multi_logloss: 0.312181\n",
      "[400]\ttraining's multi_logloss: 0.282843\tvalid_1's multi_logloss: 0.288617\n",
      "[500]\ttraining's multi_logloss: 0.272383\tvalid_1's multi_logloss: 0.278319\n",
      "[600]\ttraining's multi_logloss: 0.267357\tvalid_1's multi_logloss: 0.273385\n",
      "[700]\ttraining's multi_logloss: 0.26471\tvalid_1's multi_logloss: 0.270879\n",
      "[800]\ttraining's multi_logloss: 0.263188\tvalid_1's multi_logloss: 0.269527\n",
      "[900]\ttraining's multi_logloss: 0.262247\tvalid_1's multi_logloss: 0.268759\n",
      "[1000]\ttraining's multi_logloss: 0.261631\tvalid_1's multi_logloss: 0.268298\n",
      "[1100]\ttraining's multi_logloss: 0.261206\tvalid_1's multi_logloss: 0.268028\n",
      "[1200]\ttraining's multi_logloss: 0.260909\tvalid_1's multi_logloss: 0.267859\n",
      "[1300]\ttraining's multi_logloss: 0.260687\tvalid_1's multi_logloss: 0.267743\n",
      "[1400]\ttraining's multi_logloss: 0.260509\tvalid_1's multi_logloss: 0.267675\n",
      "[1500]\ttraining's multi_logloss: 0.260349\tvalid_1's multi_logloss: 0.267631\n",
      "[1600]\ttraining's multi_logloss: 0.260204\tvalid_1's multi_logloss: 0.267596\n",
      "[1700]\ttraining's multi_logloss: 0.260068\tvalid_1's multi_logloss: 0.267581\n",
      "[1800]\ttraining's multi_logloss: 0.259936\tvalid_1's multi_logloss: 0.267556\n",
      "[1900]\ttraining's multi_logloss: 0.259802\tvalid_1's multi_logloss: 0.267559\n",
      "[2000]\ttraining's multi_logloss: 0.259675\tvalid_1's multi_logloss: 0.267566\n",
      "[2100]\ttraining's multi_logloss: 0.259545\tvalid_1's multi_logloss: 0.267572\n",
      "[2200]\ttraining's multi_logloss: 0.259423\tvalid_1's multi_logloss: 0.267588\n",
      "[2300]\ttraining's multi_logloss: 0.259302\tvalid_1's multi_logloss: 0.267591\n",
      "[2400]\ttraining's multi_logloss: 0.259185\tvalid_1's multi_logloss: 0.267607\n",
      "[2500]\ttraining's multi_logloss: 0.259069\tvalid_1's multi_logloss: 0.267607\n",
      "[2600]\ttraining's multi_logloss: 0.258953\tvalid_1's multi_logloss: 0.26762\n",
      "[2700]\ttraining's multi_logloss: 0.258836\tvalid_1's multi_logloss: 0.267628\n",
      "[2800]\ttraining's multi_logloss: 0.258728\tvalid_1's multi_logloss: 0.267641\n",
      "Early stopping, best iteration is:\n",
      "[1806]\ttraining's multi_logloss: 0.259927\tvalid_1's multi_logloss: 0.267551\n"
     ]
    },
    {
     "data": {
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iWmsLBYtxosIYp3tFpA/Olt93OMe4jDHGhInFOFFhjNOjwKMVjTXGGFNzTqhEDGOMMdHt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\nQRAEQWn4P4FzCa/XL7BVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d41ccb5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 0 log loss 0.2675507081690603 in 1806\n",
      "Fold :  1\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.535975\tvalid_1's multi_logloss: 0.515325\n",
      "[200]\ttraining's multi_logloss: 0.369405\tvalid_1's multi_logloss: 0.343021\n",
      "[300]\ttraining's multi_logloss: 0.309893\tvalid_1's multi_logloss: 0.282651\n",
      "[400]\ttraining's multi_logloss: 0.286088\tvalid_1's multi_logloss: 0.258761\n",
      "[500]\ttraining's multi_logloss: 0.275627\tvalid_1's multi_logloss: 0.248412\n",
      "[600]\ttraining's multi_logloss: 0.270579\tvalid_1's multi_logloss: 0.243614\n",
      "[700]\ttraining's multi_logloss: 0.267913\tvalid_1's multi_logloss: 0.241232\n",
      "[800]\ttraining's multi_logloss: 0.266385\tvalid_1's multi_logloss: 0.239972\n",
      "[900]\ttraining's multi_logloss: 0.265444\tvalid_1's multi_logloss: 0.239266\n",
      "[1000]\ttraining's multi_logloss: 0.264839\tvalid_1's multi_logloss: 0.238832\n",
      "[1100]\ttraining's multi_logloss: 0.264422\tvalid_1's multi_logloss: 0.238558\n",
      "[1200]\ttraining's multi_logloss: 0.264125\tvalid_1's multi_logloss: 0.238367\n",
      "[1300]\ttraining's multi_logloss: 0.263897\tvalid_1's multi_logloss: 0.238235\n",
      "[1400]\ttraining's multi_logloss: 0.263711\tvalid_1's multi_logloss: 0.238138\n",
      "[1500]\ttraining's multi_logloss: 0.263544\tvalid_1's multi_logloss: 0.238079\n",
      "[1600]\ttraining's multi_logloss: 0.263397\tvalid_1's multi_logloss: 0.238048\n",
      "[1700]\ttraining's multi_logloss: 0.263258\tvalid_1's multi_logloss: 0.238011\n",
      "[1800]\ttraining's multi_logloss: 0.263121\tvalid_1's multi_logloss: 0.237986\n",
      "[1900]\ttraining's multi_logloss: 0.262988\tvalid_1's multi_logloss: 0.237975\n",
      "[2000]\ttraining's multi_logloss: 0.262857\tvalid_1's multi_logloss: 0.237963\n",
      "[2100]\ttraining's multi_logloss: 0.262727\tvalid_1's multi_logloss: 0.237961\n",
      "[2200]\ttraining's multi_logloss: 0.262603\tvalid_1's multi_logloss: 0.237962\n",
      "[2300]\ttraining's multi_logloss: 0.262475\tvalid_1's multi_logloss: 0.237951\n",
      "[2400]\ttraining's multi_logloss: 0.262358\tvalid_1's multi_logloss: 0.237953\n",
      "[2500]\ttraining's multi_logloss: 0.262241\tvalid_1's multi_logloss: 0.237955\n",
      "[2600]\ttraining's multi_logloss: 0.262126\tvalid_1's multi_logloss: 0.237947\n",
      "[2700]\ttraining's multi_logloss: 0.26201\tvalid_1's multi_logloss: 0.237946\n",
      "[2800]\ttraining's multi_logloss: 0.261898\tvalid_1's multi_logloss: 0.23794\n",
      "[2900]\ttraining's multi_logloss: 0.261789\tvalid_1's multi_logloss: 0.237929\n",
      "[3000]\ttraining's multi_logloss: 0.26168\tvalid_1's multi_logloss: 0.237928\n",
      "[3100]\ttraining's multi_logloss: 0.261573\tvalid_1's multi_logloss: 0.237919\n",
      "[3200]\ttraining's multi_logloss: 0.261466\tvalid_1's multi_logloss: 0.23791\n",
      "[3300]\ttraining's multi_logloss: 0.261359\tvalid_1's multi_logloss: 0.237903\n",
      "[3400]\ttraining's multi_logloss: 0.261255\tvalid_1's multi_logloss: 0.237907\n",
      "[3500]\ttraining's multi_logloss: 0.26115\tvalid_1's multi_logloss: 0.237922\n",
      "[3600]\ttraining's multi_logloss: 0.261049\tvalid_1's multi_logloss: 0.237917\n",
      "[3700]\ttraining's multi_logloss: 0.260948\tvalid_1's multi_logloss: 0.237915\n",
      "[3800]\ttraining's multi_logloss: 0.260852\tvalid_1's multi_logloss: 0.237916\n",
      "[3900]\ttraining's multi_logloss: 0.260755\tvalid_1's multi_logloss: 0.237922\n",
      "[4000]\ttraining's multi_logloss: 0.260658\tvalid_1's multi_logloss: 0.237924\n",
      "[4100]\ttraining's multi_logloss: 0.26056\tvalid_1's multi_logloss: 0.23794\n",
      "[4200]\ttraining's multi_logloss: 0.260466\tvalid_1's multi_logloss: 0.237947\n",
      "[4300]\ttraining's multi_logloss: 0.260375\tvalid_1's multi_logloss: 0.237947\n",
      "Early stopping, best iteration is:\n",
      "[3337]\ttraining's multi_logloss: 0.261319\tvalid_1's multi_logloss: 0.237901\n"
     ]
    },
    {
     "data": {
      "image/png": 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6KhPrE38V0R++kUiFY5p+/PFHNmzYwJ/+9Ce++OILBg4cyOzZs9m4cSP79+9n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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d3bcd9080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 1 log loss 0.23790060514533928 in 3337\n",
      "Fold :  2\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.533918\tvalid_1's multi_logloss: 0.534443\n",
      "[200]\ttraining's multi_logloss: 0.366865\tvalid_1's multi_logloss: 0.367371\n",
      "[300]\ttraining's multi_logloss: 0.307203\tvalid_1's multi_logloss: 0.30743\n",
      "[400]\ttraining's multi_logloss: 0.28338\tvalid_1's multi_logloss: 0.2836\n",
      "[500]\ttraining's multi_logloss: 0.272953\tvalid_1's multi_logloss: 0.27301\n",
      "[600]\ttraining's multi_logloss: 0.267945\tvalid_1's multi_logloss: 0.2679\n",
      "[700]\ttraining's multi_logloss: 0.265298\tvalid_1's multi_logloss: 0.265241\n",
      "[800]\ttraining's multi_logloss: 0.263783\tvalid_1's multi_logloss: 0.263792\n",
      "[900]\ttraining's multi_logloss: 0.262844\tvalid_1's multi_logloss: 0.262962\n",
      "[1000]\ttraining's multi_logloss: 0.26223\tvalid_1's multi_logloss: 0.262461\n",
      "[1100]\ttraining's multi_logloss: 0.26181\tvalid_1's multi_logloss: 0.262144\n",
      "[1200]\ttraining's multi_logloss: 0.261503\tvalid_1's multi_logloss: 0.261966\n",
      "[1300]\ttraining's multi_logloss: 0.26127\tvalid_1's multi_logloss: 0.261861\n",
      "[1400]\ttraining's multi_logloss: 0.261083\tvalid_1's multi_logloss: 0.261799\n",
      "[1500]\ttraining's multi_logloss: 0.260918\tvalid_1's multi_logloss: 0.261772\n",
      "[1600]\ttraining's multi_logloss: 0.260772\tvalid_1's multi_logloss: 0.261757\n",
      "[1700]\ttraining's multi_logloss: 0.260634\tvalid_1's multi_logloss: 0.261742\n",
      "[1800]\ttraining's multi_logloss: 0.260494\tvalid_1's multi_logloss: 0.261736\n",
      "[1900]\ttraining's multi_logloss: 0.260354\tvalid_1's multi_logloss: 0.261728\n",
      "[2000]\ttraining's multi_logloss: 0.26022\tvalid_1's multi_logloss: 0.261718\n",
      "[2100]\ttraining's multi_logloss: 0.260089\tvalid_1's multi_logloss: 0.261712\n",
      "[2200]\ttraining's multi_logloss: 0.25996\tvalid_1's multi_logloss: 0.26171\n",
      "[2300]\ttraining's multi_logloss: 0.259835\tvalid_1's multi_logloss: 0.261703\n",
      "[2400]\ttraining's multi_logloss: 0.259714\tvalid_1's multi_logloss: 0.261698\n",
      "[2500]\ttraining's multi_logloss: 0.259595\tvalid_1's multi_logloss: 0.261713\n",
      "[2600]\ttraining's multi_logloss: 0.259479\tvalid_1's multi_logloss: 0.261708\n",
      "[2700]\ttraining's multi_logloss: 0.259362\tvalid_1's multi_logloss: 0.261708\n",
      "[2800]\ttraining's multi_logloss: 0.259247\tvalid_1's multi_logloss: 0.261714\n",
      "[2900]\ttraining's multi_logloss: 0.259139\tvalid_1's multi_logloss: 0.261716\n",
      "[3000]\ttraining's multi_logloss: 0.259029\tvalid_1's multi_logloss: 0.261712\n",
      "[3100]\ttraining's multi_logloss: 0.258924\tvalid_1's multi_logloss: 0.26171\n",
      "[3200]\ttraining's multi_logloss: 0.258816\tvalid_1's multi_logloss: 0.261718\n",
      "[3300]\ttraining's multi_logloss: 0.258704\tvalid_1's multi_logloss: 0.261723\n",
      "[3400]\ttraining's multi_logloss: 0.258605\tvalid_1's multi_logloss: 0.261727\n",
      "Early stopping, best iteration is:\n",
      "[2405]\ttraining's multi_logloss: 0.259708\tvalid_1's multi_logloss: 0.261697\n"
     ]
    },
    {
     "data": {
      "image/png": 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yTr1SPWCtiHxShv3WBNJU9RERWQiMBXoBbYBZwGJ3vWSgPc7FxJki8rKq7izj\nMTQCdgKo6mkR+RHnY8QD/iup6ivAKwBNmjXXiZsiMimzzB5qexqvZAVv5fVSVqhceXNuSXH+zMmh\nZs2apKSk+F6bNWsWX375JWvXrvW1XaxevRrAt964cePo3bs3Xbp0KXY/4ZSWlhax9y4rL2SNxP9K\nfXVNwF4Rya9r2hjk9oXrmk6o6ikRCVjXBCAi+XVNZR20ArV0aIBlPtWrRpEZxMcblUFaWprvLwgv\n8FJeL2UFb+TNr2d69tlnC9QzDRgwgN/97nc8+OCDfPfdd2RlZXHFFSHrHTARFsqJGFuADgGWh6yu\niYKDcLB1TSXZBTQGcKugagM/lGM/xpgySE1NpUuXLmRmZhIbG8uMGTO47777+Omnnxg+fHiBOw8n\nJCRw44030qZNG/r27cvUqVOJiooqdj/G20J5puWJuqZSLAZuB1bj9CWuKPx9ljGm4pVUzxToI6zR\no0czevTooPZjvC1kg5aX6ppEZALwO6CGiOwC/ua2aswAZovINzhnWDeHMocxxpiShfQ7La/UNbkz\nC4vMLnRvX/LbkrY1xhgTPtaIYYwxxjPOqUHL6pqMqbwC9QTOnz+fhIQEqlSpwrp163zLT506xe23\n307btm2Jj4/3VTodP36cK664gqSkJBISEnjyySfDfhwmtM6pQUtVr/SrafLVNUU6lzEmcE9gYmIi\nCxYsoFu3bgWWz58/nxMnTrBp0ybWr1/PX//6V3JycqhWrRorVqxgw4YNZGRksGzZMtasWRPOwzAh\nVuZBS0QuFJF2Qa7rie5BEXlGRHa6bRv+y+8WkU3uGdlnItImEvmMORcE6gmMj4+nVatWRdYVEY4c\nOcLp06c5duwY5513HrVq1UJEiIlxvvY+deoUp06dCtR0YzwsqIkYIpKGU4sUDWQA+0Vklao+WMI2\nZ0P34Fv5NVQiMgD4C9C3pB1Z92DoeCmvl7JCZPMG0zVY2ODBg1m0aBENGzbk6NGjTJo0yTfg5eXl\n0aFDB7755hvuvfderrzyyoqObCIo2NmDtVX1PyJyJ/Caqj4pIqU1WHi+e1BV/+P3tCbFtGFY92B4\neCmvl7JCZPP6d90V1xN4+PBh1q9fT25uLrm5uUybNo0DBw4wZ84cfvrpJ4YNG0ZMTAyXXHIJAJMn\nTyY3N5fHH3+c1q1bc+mll4bxiAryQp9fPk9kVdVSf3Cul2oI/APo5C7bWMo2fwImBVg+CFgOROGc\ndX3r7jsO2OyuMwSY4rfNEiDFfazA1e7jhW6mqjhdhhl+22/HabA4H9gBNA7iOHMDLLsX+DdOBVSL\n0vbRsmVL9YqVK1dGOkKZeCmV8wmdAAAgAElEQVSvl7KqVp682dnZmpCQUGR59+7dde3atarqZL3n\nnnv09ddf970+dOhQnTdvXpHtxowZo88//3zoAgehsvxugxHJrMA6DWI8CvY7raeBD4F/q+paEWlG\n0Y/SguXrHlTVvUB+92CwCncPrlLVU+7jOL/1PlbVH9W51iq/e7DMVHWqql4GPAI8Vp59GGMqVpMm\nTVixYgWqypEjR1izZg2tW7dm//79vjsaHzt2jI8++ojWrVtHOK2pSEENWqo6X1Xbqeof3efbVXVQ\nKZudDd2D/uYC1//CfRhjihGoJ3DhwoXExsayevVq+vXrR58+fQC49957yc3NJTExkU6dOjF06FDa\ntWvHnj176NGjB+3ataNTp0706tWLa68tsVvAeEywEzFaAv8HNFDVRHf24ABVHVvCZp7vHhSRFqqa\nf0bZj/KfXRpjSlFcT+DAgQMLPE9LSyMmJob58+cXWbddu3b861//Ckk+UzkE+/HgdGAUcApAVTdS\nSg+fezY0EOjlTnnfgnNDxbdwbkOyAWdge1hVvy+0uX/34AuEoXvQ7RysISK7RGSM+9J9IrJFRDKA\nB3HKc40xxkRIsB+Z1VDV9EKz60qdaqTe7x4cVtJ2xhhjwivYM60DInIZ7pRvERkM7AlZKmPMWacs\nNU3p6ekkJyeTnJxMUlISCxcu9L32zjvvkJiYSEJCApMnTw7rMZjIC3bQuhf4K9BaRHbj3F7k7pCl\nChHrHjQmcspS05SYmMi6det8VUz//d//zenTp9m8eTPvv/8+6enpbNiwgSVLlpCVZV81n0tKHbTc\nyRAdVfU3wMVAa1Xtqqo7gti2UtU4aTHdgyXUOE3yG9y+FpHDocxnzNmsLDVNNWrUIDra+fbi+PHj\nvgv/t23bRps2bXyvd+/evcBZmDn7lfqdlqqeEZH7gLdV9UiwOz4bapxU9X/yH4vI/UD70nZkNU6h\n46W8XsoKoc9bnqqmL774gjvuuIMdO3Ywe/ZsoqOjSUxMZOPGjRw8eJDq1auzdOlSOnbsGILEprIK\ndiLGchEZDswDfAOXqv5Qwjaer3EqJBUIeJ8Dq3EKDy/l9VJWCH3e/GqgYGqa/E2dOpUdO3bw6KOP\nUrNmTc477zwGDhxIly5dqF69Ok2bNuX777+v1NVDnqhGcnkha7CD1h3un/f6LVOgWQnbJALrAyy/\nAUjGqV2qB6wVkU+CzAFOB2Caqj4iIguBsUAvoA0wC1jsrpeMc2Z0AsgUkZdVdWcZ3sdHRJoCl+JM\n0S9CVV8BXgFo0qy5TtwU0htCV5iH2p7GK1nBW3m9lBVCnzfnlhTnz5wcatasSUpKSoHX69SpQ4cO\nHYo9a5o5cyZ169b1vf7SSy8B8OijjxIbG1tkf5VJWlpapc7nzwtZg/pfqapWZNukr8YJ2Csi+TVO\npRXw5itc43RCVU+JSMAaJwARya9xKteghXNN2jtu5hJVrxpFZjk+ComEtLQ0318mXuClvF7KCpUv\nb3Z2No0bNyY6OpodO3aQmZlJXFwcAIcOHQLg22+/ZcGCBaxevTqCSU24BduIcVug5ar6egmbbQEG\nB9pdEG9ZrhonEQlVjdPNFDzLNMaUUWpqKmlpaRw4cIDY2Fieeuop6taty/3338/+/fvp168fycnJ\nfPjhh3z22WeMHz+eqlWrUqVKFf73f/+XevXqAfDkk08yevRoqlatytSpU7nwwgsjfGQmnIL9i9y/\n0PZ8oCdOS0VJg5bna5wARKQVcCFg/5wz5hcItqYJ4NZbb+XWW28NuP5LL71U6T/CMqET7MeD9/s/\nF5HawOxStlERGQhMFpGRwHGcwegBIAanxklxa5xEJM5vc/8ap82EocYJ+B1ujRPwN7+2jVRgrt/Z\nnTHGmAgp70dmR4EWpa3k9RqnQO9hjDEmcoL9Tus9fr5rbxWcmXpFK5aNMcaYEAq2xukFYKL7Mw7o\npqqPhCxViFiNkzGRY92DpiIEO2hdo6qr3J/PVXWXiDwX0mQhUFyNU6RzGXMusO5BUxGCHbR6BVh2\ndWkbVbbuweKU0D34oJt7o4h87F5kbIwpB+seNBWhxO+0ROSPwD1AMxHxv/j3ApwZfiVt6/nuQeBf\nOHVSR93fxQTgppJ2ZN2DoeOlvF7KCtY9aLyjtIkYbwEf4HyPNdJv+U+l9A7CWdA9qKor/Z6uAf4r\n0PbWPRgeXsrrpaxg3YOh5IU+v3yeyKqqQf8A9XEGhyZAk1LW/RMwKcDyQcByIArnrOtboCFOBdNm\nd50hwBS/bZYAKe5jBa52Hy8E/gFUxekyzPDbfjtQG+ei5R1A4yCOL7eE16YAj5W2j5YtW6pXrFy5\nMtIRysRLeb2UVTV8ebOzszUhIaHI8u7du+vatWuL3S4lJcX3un/WUaNG6dSpUys8Z0Xy0v8WIpkV\nWKdBjEPBTnnvD/wFuATYh9Pjtw1ICH549PFc96CI/BfQEehenu2NMWVj3YOmOMFOxBgLdAa+Vqc8\ntyelfKeF0z3YIcDykHUPUvDjzgrpHhSR3wCjgQGqeqK09Y0xgaWmptKlSxcyMzOJjY1lxowZLFy4\nkNjYWFavXk2/fv3o06cPAJ999hlJSUkkJyczcODAIt2Dbdq0oX///tY9eA4K9i/yU6p6UESqiEgV\nVV0ZxJR3z3cPikh74K9AX1XdF4kMxpwtrHvQVIRgB63DIhIDfAq8KSL7cM6GiqV6VnQPPu9mne9O\n0vhWVQeEMosxxpjiBTtoXQccwxlwbsGZ4PB0aRupx7sHVfU3JW1njDEmvIL6TktVjwCNcWbwzQL+\nhjMhwhhjilWW6qbly5fToUMH2rZtS4cOHVix4ucbhY8ePZrGjRsTExODObcFNWiJyF3AOzjf74Dz\nPdPfQxUqVKx70JjwKkt1U7169XjvvffYtGkTs2bNKvCdVv/+/UlPTw9LZlO5Bfvx4L04kyG+AFDV\nLBGpX943FZFfAZNxprqfwP2uS1WLNGW433UtUdXEwq+VlapeWcac5+Fcn5WCM1NxtKq++0tzGHOu\n6NatGzk5OQWWxcfHB1y3ffv2vscJCQkcP36cEydOUK1aNTp37hzKmMZDgh20TqjqyfzGCPe29uW6\nKaLH6p1GA/tUtaU7k7FuaRtYjVPoeCmvl7JCaPKWp7op37vvvkv79u2pVq1aBSYyZ4NgB61VIvIo\nUF1EeuH0Eb5Xzvf0TL0TcAfQ2s14BjgQaCWrcQoPL+X1UlYITd7yVjdlZ2fz2GOPMWHChCLb5OXl\neaNqyI+X8noiazC1GTjffd2Fc+PHd9zHEsy2AfbliXonoA5Og8ZfcKbczwcalHZ8VuMUOl7K66Ws\nqqHNW5bqpp07d2qLFi30s88+C7ivmjVr2u82hLxQ41TiRAwRaeIObGdUdbqq/lZVB7uPy/XxYAl8\n9U6quhfIr3cKVuF6p1Wqesp9HOe33seq+qOqHgfy650CiQZigc9V9XJgNc7NMI0xIXD48GH69evH\nuHHj+PWvfx3pOKaSKm32oG+GoIhU1AQEr9Q7HQSO4py1gXOmdXkQGY0xrrJUN02ZMoVvvvmGP//5\nz767Fu/b5xTRPPzww8TGxnL06FF++9vfMmbMmAgelYmk0r7T8h9ImlXQe3qi3klVVUTew5k5uAKn\nb3FrqN7PmLNRWaqbHnvsMR577LGA60+YMIEJEyYAzndlVuN07ipt0NJiHpebOxh4ot4JeASYLSKT\ngf3A0BC/nzHGmBKUNmglich/cM64qruPcZ+rqtYqz5uqd+qdduCc8RljjKkEShy0VDUqXEGMMcaY\n0gR7P62zmtU7GVPxytI7ePDgQXr06EFMTAz33Xdfgf2cPHmSP/zhD7Rs2ZLWrVuzatWqsB2DqXxC\nOmiJyK9EZK6I/FtEtorIUhFpWcy6cSKyOZR5SvARcBHQXFWT3Z9NItJNRL4UkdMiMjhC2YzxpLL0\nDp5//vn8+c9/5oUXil5V8swzz1C/fn2+/vprtm7dSnJyckhzm8qtXHfzDYbH6prew+kYzCq0/Fuc\nC5KHhzuQMV5Xlt7BmjVr0rVrV7755psir7366qt89dVXAFSpUoXatWtXeFbjHSEbtPBQXZOqrnHf\np/DyHHf5mWAP2roHQ8dLeb2UFSo+7y/pHfR3+PBhAB5//HHS0tK47LLLSE1NrZB9G28K5aCVCKwP\nsPwGIBmnXqkesFZEPinDfmsCaar6iIgsBMYCvYA2wCxgsbteMtAe52LiTBF5WVV3lutISmHdg+Hh\npbxeygoVn7e8vYNfffUVu3fv9q3/448/smvXLmrXrs1f/vIX3n77bV5++WXq1i21u7rS8ESfn8sL\nWUM5aBXHV9cE7BWR/LqmjUFuX7iu6YSqnhKRgHVNACKSX9cUkkFLVV8BXgFo1aqV3n/LdaF4mwqX\nlpbGjR66SNNLeb2UFUKXNycnh5o1axa5GLhOnTp06NCBjh07Flk/NzfXt76qUqNGDR5//HGqVKnC\nZZddxlVXXeWpi4u9dDG0F7KGciKGV+qajDGVlIjQv39/37/+P/74Y+Li4iKayURWKAetFUA1967H\nQJG6pigRuRjn4t3CtyTNAZJFpIqINCaEdU3GmNAoS+8gQFxcHA8++CAzZ84kNjaWrVud1rTnnnuO\nMWPG0K5dO2bPns0f//jHSB2SqQRCdvbhpbomEZkA/A6oISK7gL+p6hh3kF0IXAj0F5GnVDUhlFmM\nOVuUpXcQKDLTMF/Tpk355JOfv/au7N+5mNAK6UdmHqprehgoMrtQVdfi3J7EGGNMJWCNGMYYYzzj\nnBq0rK7JmNAqS3UTwLhx42jevDmtWrXiww8/9C1ftmwZrVq1onnz5owfPz5s+U3ld04NWqp6pV9N\nk6+uKdK5jDlblKW6aevWrcydO5ctW7awbNky7rnnHvLy8sjLy+Pee+/lgw8+YOvWrcyZM8c3KcOY\niAxaXugkFJELCp2RHXDvq2WMKUa3bt2KXPgbHx9Pq1atiqy7aNEibr75ZqpVq8all15K8+bNSU9P\nJz09nebNm9OsWTPOO+88br75ZhYtWhSuQzCVXNivXfJKJ6Gq/oTTqgGAiKwHFpS2ndU4hY6X8nop\nK1RM3rJWN+3evZvOnTv7nsfGxrJ7924AGjduXGD5F1988YuymbNHJC649Uwnod/7tgDqA58W87rV\nOIWBl/J6KStUTN6yVjft2rWLbdu2+dbbs2cPW7ZsQVXZs2ePb/m2bdv47rvvfM+9UDXkz0t5vZA1\nEoOWFzsJU4F5fk0cBViNU3h4Ka+XskLF5g22umn16tUAvvXGjRtH7969fa/lL1+9ejWdOnXyPfdC\n1ZA/L+X1QtbKNBHD10moqnuB/E7CYBXuJFylqqfcx3F+632sqj+q6nEgv5OwNDcDga+UNMaUy4AB\nA5g7dy4nTpwgOzubrKwsrrjiCjp16kRWVhbZ2dmcPHmSuXPnMmDAgEjHNZVEJM60tgCBbqgYsk5C\nESl3J6GIJAHRqhro7NAY4yc1NZW0tDQOHDhAbGwsTz31FHXr1uX+++9n//799OvXj+TkZD788EMS\nEhK48cYbadOmDdHR0UydOpWoqCgApkyZQp8+fcjLy+OOO+4gIcGKaIwjEoPWCuBZEblLVadDkU7C\nWUBdnE7CERQcmHKAe0SkCtCI8HQSpmJnWcYEpazVTaNHj2b06NFFll9zzTVcc801FZrNnB3CPmh5\nqZPQdSNg/+8xxphKICK36/BKJ6G7TrPS1jHGGBMelWkihjHGwwJVOP3www/06tWLFi1a0KtXLw4d\nOgTAoUOHGDhwIO3ateOKK65g8+bNJe7HmHw2aGGdhMZUhEAVTuPHj6dnz55kZWXRs2dPX4/gs88+\nS3JyMhs3buT1119n2LBhJe7HmHxW40TxnYQikiYimX4DWf1Q5jDGywJVOC1atIjbb78dgNtvv52/\n//3vgNM72LNnTwBat25NTk4Oe/fuLXY/xuSzGqfS3aKq60pfzWE1TqHjpbxeygq/LG9J9U179+6l\nYcOGADRs2JB9+/YBkJSUxIIFC+jatSvp6ens2LGDXbt20aBBg3JlMOeOSJxpBaxxAj4TkedFZLOI\nbBKRmwpvKCJDRGSK3/MlIpLiPs4VkedEZL2IfCQiV7hnSttFZIDf9gtEZJmIZLl3LDbGhNnIkSM5\ndOgQycnJvPzyy7Rv357o6IjMCzMeYzVOpdc4vSYiecC7OH2IRaqcrHswPLyU10tZ4Zfl9e+qK9w7\nWKtWLd59910uuugiDh48yAUXXOB77fbbb+f2229HVUlNTWXXrl2+iRrF9ReCN/rx/HkpryeyqmpY\nf4A/AZMCLJ8E3OH3fDZOuW0csNldNgSY4rfOEiDFfXwCEPfx08Bo93EV4LDf9tP9tv8A6FpC1kbu\nnxcA/wBuK+34WrZsqV6xcuXKSEcoEy/l9VJW1YrLm52drQkJCb7nw4cP13Hjxqmq6rhx43TEiBGq\nqnro0CE9ceKEqqq+8soreuutt5a4n1BkDRcv5Y1kVmCdBjGGROLjwS1AhwDLQ1bjRMEzyqBrnFR1\nt/vnT8BbhKeBwxhPSk1NpUuXLmRmZhIbG8uMGTMYOXIky5cvp0WLFixfvpyRI0cCTnN7QkICrVu3\n5oMPPuDFF18scT/G5LMap2K4fYV1VPWAiFQFrgU+CtX7GeN1xVU4ffzxx0WWdenShaysrDLtxxiw\nGqeSVAM+dAesKJwBa3oI388YY0wprMap+IxHCPwxpjHGmAixRgxjjDGeYYMWVuNkTHmUpWsQnKnx\nycnJJCQk0L17d9/ySZMmkZCQQGJiIqmpqRw/fjysx2G8xQYtiq9xinQuYyqzsnQNHj58mHvuuYfF\nixezZcsW5s+fD8Du3bt56aWXWLduHZs3byYvL4+5c+eG/ViMd1j3YAnc5owNIrJFRKaJSFQkchhT\nGZWla/Ctt97ihhtuoEmTJgDUr/9zjefp06c5duwYp0+f5ujRo1xyySVhOgLjRdY9WLIbVfU/buZ3\ngN8CJf4z0LoHQ8dLeb2UFcqet7i+weK6Br/++mtOnTpFSkoKP/30E8OGDeO2226jUaNGDB8+nCZN\nmlC9enV69+5N7969f/kBmbNWJGYPBuweFMfzwNU4U97Hquo8/w1FZAjQUVXvc58vAV5Q1TQRyQWm\nAr/BuebrUWAC0AR4QFUXu9sPAGoAlwELVfXh4oKq6n/ch9HAeW6uIqzGKTy8lNdLWaHsefOrfgrX\nLZ0+fbpADVD+8x07dpCZmcnEiRM5efIk9957LyJCnTp1mDVrFm+88QYxMTGMGTOG0aNH06tXr2Lf\n2xNVQ368lNcLWa17sJTuQRH5EOci5g9wzraKUNVXgFcAWrVqpfffcl0ZYkdOWloaN6akRDpG0LyU\n10tZofx5c3JyqFmzJinuto0aNaJVq1Y0bNiQPXv2cMkll5CSksKaNWtISkri6quvBmDx4sWcf/75\nHD9+nPbt23P99dcD8N1337FmzRrf/orLWtLrlY2X8noha2WaiNEVmKOqeaq6F1gFdCrD9ieB/G+F\nNwGrVPWU+zjOb72PVfVHVT0ObAWalrRTVe0DNMS52Pj/lSGPMeecAQMGMGvWLABmzZrFddc5/4C7\n7rrr+PTTT33fW33xxRfEx8fTpEkT1qxZw9GjR1FVPv74Y+Lj4yN5CKaSi8SZ1hZgcIDlIesedCuZ\n8gXdPZhPVY+LyGLgOmB5EDmNOeulpqaSlpbGgQMHiI2N5amnnmLkyJHceOONzJgxgyZNmvhmCcbH\nx9O3b1/atWtHlSpVuPPOO31T5QcPHszll19OdHQ07du35w9/+EMkD8tUctY9WAwRiQEuUNU97qB3\nDfBpqN7PGK8pS9cgwIgRIxgxYkSR5U899RRPPfVUhWYzZy/rHixeTWCxiFTD6R5cAUwreRNjjDGh\nZN2DxWfcS9m+UzPGGBNilWkihjGmEitrbRPA2rVriYqK4p13fp54++2339K7d2/i4+Np06YNOTk5\n4ToEcxawQQvrHjQmGGWpbQLIy8vjkUceoU+fPgW2ue222xgxYgTbtm0jPT29QDuGMaUJ6aDllbom\nnHtlXQQ0D9Q9KCKDRURFpGOE8hkTcWWpbQJ4+eWXGTRoUIFBaevWrZw+fdp38XBMTAw1atQIQ3pz\ntgjZd1oeq2t6D5gCFLmVqohcAPwJ+CKYHVmNU+h4Ka+XskLpecta27R7924WLlzIihUrWLt2rW/9\nr7/+mjp16nDDDTeQnZ3Nb37zG8aPH09UlNV6muCEciKGl+qa1rjvE+jlP7v7H17c9lbjFB5eyuul\nrFB63rLWNo0ZM4abbrqJTz/9lO+//54tW7ZQr149NmzYQFpaGq+88goNGjTwXdvVr1/gQTEQL1QN\n+fNSXi9kDeWg5am6pkBEpD3QWFWXiEixg5Z/jVOTZs114qaITMoss4fansYrWcFbeb2UFUrPm3NL\nivNnkLVNO3bsYMKECQAcOHCAL7/8kqSkJPr06cPKlSv53e9+BwRX21SYF6qG/HkprxeyRuL/Vb66\nJmCviOTXNW0McvvCdU0nVPWUiASsawIQkfy6pqAHLfcC5knAkGC3AaheNYrMYj5KqWzS0tJ8fxl5\ngZfyeikrlD9vfm3TyJEjC9Q2ZWdn+9YZMmQI1157Lddffz15eXkcOnSI/fv3c/HFF7NixQo6drSv\nik3wQjkRYwvQIcDykNU1UXAQLnNdUyEX4JwtpolIDtAZ52Jj+3+YOSelpqbSpUsXMjMziY2NZcaM\nGYwcOZLly5fTokULli9fzsiRI0vcR1RUFC+88AI9e/akbdu2qCp33XVXmI7AnA1Ceablibqm4rhn\nafXyn4tIGjBcVdeFO4sxlUFZa5vyzZw5s8DzXr16sXFjsB+sGFNQyM603LOhgUAvd8r7FmAM8BbO\nR4EbcAa2h1X1+0Kb+9c1vUBo65oQkQkisguoISK7RGRMKN/PGGNM+YT0Oy0v1DW5rz8MFDu70F0n\npaTXjTHGhJ41YhhjjPGMc2rQsromY4r34osvkpiYSEJCApMnTwZgw4YNdOnShbZt29K/f3/+85//\nAHDy5EmGDh1K27ZtSUpKqvTX9pizxzk1aKnqlX41TUXqmow5V2VnZzN9+nTS09PZsGEDS5YsISsr\nizvvvJPx48ezadMmBg4cyPPPPw/A9OnTAdi0aRPLly/noYce4syZM5E8BHOOiMig5YVOQhGpISLv\ni8hXIrJFRMaXvpUx3rRjxw46d+5MjRo1iI6Opnv37ixcuJDMzEy6desGOLP+3n33XcDpEOzZsycA\n9evXp06dOqxbZxNrTeiF/eJij3USvqCqK0XkPOBjEblaVT8oaQPrHgwdL+X1UlaAp+Iv5a233uLg\nwYNUr16dpUuX0rFjRxITE1m8eDHXXXcd8+fPZ+dO5/r8pKQkFi1axM0338zOnTtZv349O3fu5Ior\nwn51ijnHyM/X6YbpDUX+HzBGVbsVWi44HX8FOgndOxcvUdXEcHcSFsr3IrA5/5qzQq/5dw92eGJy\nkVUqpQbVYe+xSKcInpfyeikrwKW1o1i1ahWLFi2ievXqNG3alGrVqtG/f39efvllfvzxR37961+z\nYMECFi1aRF5eHtOmTeNf//oXDRo0IC8vj2uvvZauXbuGPGtubi4xMTGlr1hJeClvJLP26NFjvaqW\nXt6gqmH9wWlMnxRg+SBgOc6t7RsA3wINcaqZNrvrDAGm+G2zBEhxHytwtft4IfAPoCpOx2GG3/bb\ngdo4FzPvwOkWLC1zHXe7ZqWt27JlS/WKlStXRjpCmXgpr5eyqhbNO2rUKJ06dWqBZZmZmdqpU6eA\n23fp0kW3bNkSqngFeP13W5lFMiuwToMYQyrTRAxfJ6E6t7rP7yQMVuFOwlWqesp9HOe33seq+qOq\nHgfyOwmLJSLRwBzgJVXdXoY8xnhK/m1Fvv32WxYsWEBqaqpv2ZkzZxg7dix33303AEePHuXIkSMA\nLF++nOjoaNq0aROZ4OacEonC3C3A4ADLQ9ZJ6A48+craSfgKkKWqk4PIZ4xnDRo0iIMHD1K1alWm\nTp3KhRdeyIsvvsjUqVMBuOGGGxg6dCjgDHB9+vShSpUqNGrUiNmzZ0cyujmHRGLQ8kwnoYiMxfko\n8c5Qvo8xlcGnn35aZNmwYcMYNmxYkeVxcXFkZmaGI5YxBYR90FJVFZGBwGQRGQkcxxmMHgBicDoJ\nFbeT0J2Ikc+/k3AzIewkFJFYYDTwFfCle4PIKar6t1C9pzHGmJJF5C516oFOQlXdRXAfWRpjjAmT\nyjQRwxgTIe+8806RCqeMjAw6d+5McnIyHTt2JD09HYBDhw4xcOBA2rVrxxVXXMHmzWG/9t+cw2zQ\nwjoJzblt8+bNvP/++0UqnB5++GGefPJJMjIyePrpp3n4YeeSxmeffZbk5GQ2btzI66+/HvA7L2NC\nxWqcKL6TUERuEpGNbo3ThFBmMCZStm3bRps2bYpUOImIryD3xx9/5JJLLgEKVji1bt2anJwc9u7d\nG7H85txiNU7FEJGLgOeBDqq6X0RmiUhPVS3xNq1W4xQ6Xsrrlaw54/uRmJjIxo0bi1Q4TZ48mT59\n+jB8+HDOnDnDP//5T8CpcFqwYAFdu3YlPT2dHTt2sGvXLho0aBDhozHnAqtxKqbGyZ2GP05Vf+M+\nvxXooqr3BFjXapzCwEt5vZK1baPaACxYsIBly5YVqHDKy8sjKSmJ7t27s3LlSpYsWcLEiRM5cuQI\nU6ZMISsri2bNmvHtt98yfPhwmjdvHpbMXqpFAm/l9UKNUyQGrT8Bl6rq/xRaPgi4G+gL1APWAlcC\n1Qhu0FLgGlX9QEQWAjWBfkAbnLO6ZHf7J4D2OBcZZwJdVXVngJwX4kyt7wrsAuYB56lq/5KOr0mz\n5lrlxhfL8ZsJv4fanmbipohMIC0XL+X1Stac8f0ASEtLIyUlBYBHH32U2NhYRo0axeHDhxERVJXa\ntWv7Pi7Mp6pceumlbGKQh/4AAApzSURBVNy4kVq1aoUls39WL/BS3khmFZGgBq3K9P8qX40TsFdE\n8mucNga5feEapxOqekpEAtY4AYhIfo1TkUFLVQ+JyB9xBqszwD+BZqWFqF41ikz3L4LKLi0tjZxb\nUiIdI2heyuulrODMCISfK5xWr17Nyy+/zKpVq0hJSWHFihW0aNECgMOHD1OjRg3OO+88/va3v9Gt\nW7ewDVjGWI1TCb8DVX0PeA98HwHmBZHRGM958sknGT16dIEKp+nTpzNs2DBOnz7N+eefzyuvvAI4\nEzduu+02oqKiaNOmDTNmzIhwenMusRqnEohIfVXd535UeA+BL4g2xvNeeumlIh8Lde3alfXr1xdZ\nt0uXLmRlZYUpmTEFWY1TyV4UkST38dOqWmlmNxpjzLnIapxKzpla0uvGGGPCyxoxjDHGeEZlmj0Y\nMSLyBc7Uen+3quqmSOQxxhgTmA1aODVOkc5gjDGmdPbxoDHGGM+wQcsYY4xnhL3G6WwnIj/h1EN5\nQT3gQKRDlIGX8nopK3grr5eygrfyRjJrU1W9uLSV7DutipcZTH9WZSAi67ySFbyV10tZwVt5vZQV\nvJXXC1nt40FjjDGeYYOWMcYYz7BBq+K9EukAZeClrOCtvF7KCt7K66Ws4K28lT6rTcQwxhjjGXam\nZYwxxjNs0DLGGOMZNmhVIBHpKyKZIvKNe9uVSOdpLCIrRWSbiGwRkWHu8jEisltEMtyfa/y2GeXm\nzxSRPmHOmyMim9xM69xldUVkuYhkuX9e6C4XEXnJzbpRRC4Pc9ZWfr+/DBH5j4g8UFl+tyLyqojs\nE5HNfsvK/LsUkdvd9bNE5PYw531eRL5yMy0UkTru8jgROeb3O57mt00H939D37jHFMzNZSsia5n/\nu4fj74tiss7zy5kjIhnu8oj+XoOmqvZTAT9AFPBvoBlwHs59wdpEOFND4HL38QXA10AbYAwwPMD6\nbdzc1YBL3eOJCmPeHKBeoWUTgJHu45HAc+7ja4APcO543Rn4IsL/7b8HmlaW3y3OTVQvBzaX93eJ\nczPW7e6fF7qPLwxj3t5AtPv4Ob+8cf7rFdpPOtDFPZYPgKvDlLVM/93D9fdFoKyFXp8IPFEZfq/B\n/tiZVsW5AvhGVber6klgLnBdJAOp6h5V/dJ9/BOwDeeOz8W5DpirqidUNRv4hhDfHToI1wGz3Mez\ngOv9lr+ujjVAHRFpGImAQE/g36q6o4R1wvq7VdVPgB8CZCjL77IPsFxVf1DVQ8ByoG+48qrq/2/v\nTEOtqqI4/vunZZj2TLOIRnsYYZOVkDRahFSUWFlYUpnSXBBR9sHqg0gEUkS8Jppeg5WZWtaHNAwl\nM8PxqaXNhuJDoVEtInX1Ya/jO0+v1zfeQdcPNnefdc+w9jr3nXX28NaabWbbfHMhcEyxc7jOh5rZ\nl5aetG/Q1MZO1bUIe7rvJXleFNPVe0vXA+8UO0ep7NpSwml1HEcD63Lb6ynuIEqKUgboM4GvXHSv\nD7u8mg0TUf42GDBb0hJJt7vsSDNrhOSEgSNcXm5d84yk+R9+JdoWWm/LStA5YwzpDT+jn6RlkuZJ\nusBlR5N0zCi1vq2575Vg2wuAjWb2fU5WiXZtRjitjqPQGG9F/D+BpB7ANOB+M/sLeB6oBQYCjaQh\nAih/G84zs7OAy4F7JF1YZN9y65qUkA4ChgFTXVSpti3GnnSrCJ0ljQe2AZNd1AgcZ2ZnAg8Ab0s6\nlPLq29r7Xgm2vYHmL1uVaNfdCKfVcawHjs1tHwNsKJMuO5F0IMlhTTaz6QBmttHMtpvZDuAlmoap\nytoGM9vgn5uAGa7XxmzYzz83VYKuOS4HlprZRqhc2zqttWXZdfbFH1cCo3xoCh9q+9XrS0hzQye5\nvvkhxJLp24b7XlbbSuoKXANMyWSVaNdChNPqOBYB/SX187fvkcDMcirkY9avAKvN7KmcPD/3czWQ\nrSyaCYyU1E1SP6A/aQK2FLoeIqlnVidNwq9ynbJVa7cAH+Z0vdlXvg0G/syGvkpMs7fVSrRtjtba\nchYwVNJhPtw11GUlQdJlwMPAMDP7OyfvK6mL108k2fIn13mzpMH+278518bO1rW1973cz4tLgTVm\ntnPYrxLtWpByrQDZFwtpFdZ3pDeU8RWgz/mkbvwKYLmXK4A3gZUunwkclTtmvOv/LSVcIURaRdXg\n5evMfkAfYA7wvX/2drmAZ13XlcCgMti3O/ArUJOTVYRtSY60EfiP9KY8ti22JM0l/eDl1hLr+wNp\n3if77b7g+17rv5EGYClwVe48g0gO40egDo/6UwJdW33fS/G8KKSry+uBO3fZt6x2bWmJME5BEARB\n1RDDg0EQBEHVEE4rCIIgqBrCaQVBEARVQzitIAiCoGoIpxUEQRBUDeG0gqCFSNqu5pHdT2jDOXpJ\nurvjtdt5/mGdFTG8yDWHSxpQymsG+y+x5D0IWoikLWbWo53nOAH42MxObeVxXcxse3uu3Rl4ZIWX\nSW16v9z6BPs+0dMKgnYgqYtS3qdFHiz1Dpf3kDRH0lLPQ5RF8H4CqPWe2iRJQyR9nDtfnaTRXl8r\n6TFJ84HrJNVK+sQDCn8u6eQC+oyWVOf1eknPK+VU+0nSRR7MdbWk+twxWyQ96brOkdTX5QMlLVRT\nPqss/9ZcSY9LmodHrAAmeZtqJd3m9miQNE1S95w+z0ha4PqMyOkwzu3UIOkJl+21vcF+SLn+qzlK\nlGorwHaaojPMcNntwCNe7wYsJuVN6kpK5wBwOCm6g9glZxEwhNRLybbrgNFeXwuMy303B+jv9XOA\nzwroOBqo83o9KeWFSGkv/gJOI72sLgEG+n5Giu0H8Fju+BXARV6fADzt9bnAc7lr1gMjctt9cvWJ\nwH25/ab69QeQUnNAit+4AOju271b2t4o+1/pulevFgRBxj9mNnAX2VDg9FyvoYYUs2098LhSpPod\npFQOR7bhmlNgZ6T+c4Gpakoa260Fx39kZiZpJSkNxUo/39ckB7rc9csCp74FTJdUA/Qys3kuf52m\nSPbk9i/EqZImAr2AHjSPV/iBpaCy30jK7HEp8Jp5fEEz+60d7Q32ccJpBUH7EKkn0SyQrA/x9QXO\nNrP/JK0FDi5w/DaaD9Pvus9W/zwA+KOA09wb//rnjlw9297T339LJrq3FvmuHhhuZg1uhyEF9IGm\nlBcqcM22tjfYx4k5rSBoH7OAu5RSwCDpJKUo9TXAJndYFwPH+/6bgZ65438BBngU8BpSFuTdsJQH\n7WdJ1/l1JOmMDmrDAUDWU7wRmG9mfwK/qykR4E3AvEIHs3ubegKNbpNRLbj+bGBMbu6rdye3N6hi\nwmkFQft4GfgGWCppFfAiqQczGRgkaTHpwb0GwFK+oi8krZI0yczWAe+R5o8mA8uKXGsUMFZSFgm/\no9KzbwVOkbQEuIQ0fwUpfckkSStIyQ0n7OH4d4GHlDLe1gKPkjJkf4q3uxhm9gkpMvpiScuBB/2r\nzmpvUMXEkvcg2M/piKX8QVAqoqcVBEEQVA3R0wqCIAiqhuhpBUEQBFVDOK0gCIKgaginFQRBEFQN\n4bSCIAiCqiGcVhAEQVA1/A8RzgF8LBZj7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d4b64be80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 2 log loss 0.26169731449645584 in 2405\n",
      "Fold :  3\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.532914\tvalid_1's multi_logloss: 0.542419\n",
      "[200]\ttraining's multi_logloss: 0.365586\tvalid_1's multi_logloss: 0.378324\n",
      "[300]\ttraining's multi_logloss: 0.305851\tvalid_1's multi_logloss: 0.3196\n",
      "[400]\ttraining's multi_logloss: 0.282004\tvalid_1's multi_logloss: 0.29591\n",
      "[500]\ttraining's multi_logloss: 0.271559\tvalid_1's multi_logloss: 0.285432\n",
      "[600]\ttraining's multi_logloss: 0.266544\tvalid_1's multi_logloss: 0.280403\n",
      "[700]\ttraining's multi_logloss: 0.263901\tvalid_1's multi_logloss: 0.27776\n",
      "[800]\ttraining's multi_logloss: 0.262391\tvalid_1's multi_logloss: 0.276345\n",
      "[900]\ttraining's multi_logloss: 0.261452\tvalid_1's multi_logloss: 0.275547\n",
      "[1000]\ttraining's multi_logloss: 0.260839\tvalid_1's multi_logloss: 0.275096\n",
      "[1100]\ttraining's multi_logloss: 0.260413\tvalid_1's multi_logloss: 0.274825\n",
      "[1200]\ttraining's multi_logloss: 0.260106\tvalid_1's multi_logloss: 0.274657\n",
      "[1300]\ttraining's multi_logloss: 0.259873\tvalid_1's multi_logloss: 0.274558\n",
      "[1400]\ttraining's multi_logloss: 0.259684\tvalid_1's multi_logloss: 0.274487\n",
      "[1500]\ttraining's multi_logloss: 0.259516\tvalid_1's multi_logloss: 0.274457\n",
      "[1600]\ttraining's multi_logloss: 0.259367\tvalid_1's multi_logloss: 0.274439\n",
      "[1700]\ttraining's multi_logloss: 0.259226\tvalid_1's multi_logloss: 0.274432\n",
      "[1800]\ttraining's multi_logloss: 0.25909\tvalid_1's multi_logloss: 0.274423\n",
      "[1900]\ttraining's multi_logloss: 0.258955\tvalid_1's multi_logloss: 0.274427\n",
      "[2000]\ttraining's multi_logloss: 0.258825\tvalid_1's multi_logloss: 0.274452\n",
      "[2100]\ttraining's multi_logloss: 0.258701\tvalid_1's multi_logloss: 0.274453\n",
      "[2200]\ttraining's multi_logloss: 0.258575\tvalid_1's multi_logloss: 0.274456\n",
      "[2300]\ttraining's multi_logloss: 0.258448\tvalid_1's multi_logloss: 0.274467\n",
      "[2400]\ttraining's multi_logloss: 0.258324\tvalid_1's multi_logloss: 0.274448\n",
      "[2500]\ttraining's multi_logloss: 0.25821\tvalid_1's multi_logloss: 0.274445\n",
      "[2600]\ttraining's multi_logloss: 0.258094\tvalid_1's multi_logloss: 0.274459\n",
      "[2700]\ttraining's multi_logloss: 0.257978\tvalid_1's multi_logloss: 0.274452\n",
      "Early stopping, best iteration is:\n",
      "[1770]\ttraining's multi_logloss: 0.259131\tvalid_1's multi_logloss: 0.274418\n"
     ]
    },
    {
     "data": {
      "image/png": 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06NGDL76I6kEd4wNVqiZLRFYCNQtN/r2qrvcijzGmoNJWY7377rukp6eTnp7O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\nXmObfq8fBrzVl4JLaAVazWyTr68mDXK14P/Lgb1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d75776b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 3 log loss 0.274417589877202 in 1770\n",
      "Fold :  4\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.534982\tvalid_1's multi_logloss: 0.526302\n",
      "[200]\ttraining's multi_logloss: 0.368161\tvalid_1's multi_logloss: 0.356563\n",
      "[300]\ttraining's multi_logloss: 0.308529\tvalid_1's multi_logloss: 0.295882\n",
      "[400]\ttraining's multi_logloss: 0.284675\tvalid_1's multi_logloss: 0.27179\n",
      "[500]\ttraining's multi_logloss: 0.274202\tvalid_1's multi_logloss: 0.261417\n",
      "[600]\ttraining's multi_logloss: 0.269166\tvalid_1's multi_logloss: 0.256502\n",
      "[700]\ttraining's multi_logloss: 0.266508\tvalid_1's multi_logloss: 0.253996\n",
      "[800]\ttraining's multi_logloss: 0.264979\tvalid_1's multi_logloss: 0.252615\n",
      "[900]\ttraining's multi_logloss: 0.264034\tvalid_1's multi_logloss: 0.251839\n",
      "[1000]\ttraining's multi_logloss: 0.263416\tvalid_1's multi_logloss: 0.251392\n",
      "[1100]\ttraining's multi_logloss: 0.262992\tvalid_1's multi_logloss: 0.251119\n",
      "[1200]\ttraining's multi_logloss: 0.262683\tvalid_1's multi_logloss: 0.250956\n",
      "[1300]\ttraining's multi_logloss: 0.262442\tvalid_1's multi_logloss: 0.250861\n",
      "[1400]\ttraining's multi_logloss: 0.262253\tvalid_1's multi_logloss: 0.250816\n",
      "[1500]\ttraining's multi_logloss: 0.262091\tvalid_1's multi_logloss: 0.250775\n",
      "[1600]\ttraining's multi_logloss: 0.261945\tvalid_1's multi_logloss: 0.25075\n",
      "[1700]\ttraining's multi_logloss: 0.261805\tvalid_1's multi_logloss: 0.250738\n",
      "[1800]\ttraining's multi_logloss: 0.261664\tvalid_1's multi_logloss: 0.250743\n",
      "[1900]\ttraining's multi_logloss: 0.261525\tvalid_1's multi_logloss: 0.250758\n",
      "[2000]\ttraining's multi_logloss: 0.261395\tvalid_1's multi_logloss: 0.250752\n",
      "[2100]\ttraining's multi_logloss: 0.261268\tvalid_1's multi_logloss: 0.250765\n",
      "[2200]\ttraining's multi_logloss: 0.261143\tvalid_1's multi_logloss: 0.250764\n",
      "[2300]\ttraining's multi_logloss: 0.261016\tvalid_1's multi_logloss: 0.250759\n",
      "[2400]\ttraining's multi_logloss: 0.260888\tvalid_1's multi_logloss: 0.250757\n",
      "[2500]\ttraining's multi_logloss: 0.260775\tvalid_1's multi_logloss: 0.250761\n",
      "[2600]\ttraining's multi_logloss: 0.260655\tvalid_1's multi_logloss: 0.250772\n",
      "Early stopping, best iteration is:\n",
      "[1689]\ttraining's multi_logloss: 0.261823\tvalid_1's multi_logloss: 0.250735\n"
     ]
    },
    {
     "data": {
      "image/png": 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SKxqW3jIqXiPIDEUROQvn0SQXqmoHnEkkxhhT4cqTIwgwceJErrzySho0aFCozerVq9m5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\npZmtdfOrksb68bHAIbdXs8YPAbMl7QMeJk0RLgH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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d760084a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 4 log loss 0.25073535394617447 in 1689\n",
      "Fold :  5\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.533078\tvalid_1's multi_logloss: 0.540784\n",
      "[200]\ttraining's multi_logloss: 0.365863\tvalid_1's multi_logloss: 0.375397\n",
      "[300]\ttraining's multi_logloss: 0.306173\tvalid_1's multi_logloss: 0.316198\n",
      "[400]\ttraining's multi_logloss: 0.282338\tvalid_1's multi_logloss: 0.292659\n",
      "[500]\ttraining's multi_logloss: 0.271897\tvalid_1's multi_logloss: 0.282353\n",
      "[600]\ttraining's multi_logloss: 0.266873\tvalid_1's multi_logloss: 0.277437\n",
      "[700]\ttraining's multi_logloss: 0.264227\tvalid_1's multi_logloss: 0.274907\n",
      "[800]\ttraining's multi_logloss: 0.262706\tvalid_1's multi_logloss: 0.273507\n",
      "[900]\ttraining's multi_logloss: 0.261762\tvalid_1's multi_logloss: 0.272706\n",
      "[1000]\ttraining's multi_logloss: 0.261146\tvalid_1's multi_logloss: 0.272249\n",
      "[1100]\ttraining's multi_logloss: 0.260721\tvalid_1's multi_logloss: 0.271978\n",
      "[1200]\ttraining's multi_logloss: 0.260418\tvalid_1's multi_logloss: 0.271826\n",
      "[1300]\ttraining's multi_logloss: 0.260185\tvalid_1's multi_logloss: 0.27174\n",
      "[1400]\ttraining's multi_logloss: 0.259995\tvalid_1's multi_logloss: 0.271692\n",
      "[1500]\ttraining's multi_logloss: 0.259835\tvalid_1's multi_logloss: 0.271669\n",
      "[1600]\ttraining's multi_logloss: 0.259686\tvalid_1's multi_logloss: 0.271652\n",
      "[1700]\ttraining's multi_logloss: 0.259546\tvalid_1's multi_logloss: 0.271656\n",
      "[1800]\ttraining's multi_logloss: 0.259401\tvalid_1's multi_logloss: 0.271666\n",
      "[1900]\ttraining's multi_logloss: 0.259269\tvalid_1's multi_logloss: 0.27168\n",
      "[2000]\ttraining's multi_logloss: 0.259133\tvalid_1's multi_logloss: 0.271695\n",
      "[2100]\ttraining's multi_logloss: 0.259003\tvalid_1's multi_logloss: 0.271709\n",
      "[2200]\ttraining's multi_logloss: 0.258875\tvalid_1's multi_logloss: 0.27173\n",
      "[2300]\ttraining's multi_logloss: 0.25875\tvalid_1's multi_logloss: 0.271738\n",
      "[2400]\ttraining's multi_logloss: 0.258626\tvalid_1's multi_logloss: 0.271752\n",
      "[2500]\ttraining's multi_logloss: 0.258506\tvalid_1's multi_logloss: 0.271781\n",
      "[2600]\ttraining's multi_logloss: 0.25839\tvalid_1's multi_logloss: 0.271796\n",
      "Early stopping, best iteration is:\n",
      "[1600]\ttraining's multi_logloss: 0.259686\tvalid_1's multi_logloss: 0.271652\n"
     ]
    },
    {
     "data": {
      "image/png": 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gu+++IzMzkxdffJEzZ85w9913IyI0a9YsrL3k5OTg8XjC+pmhFI39WIxTCTFO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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d4d69dda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 5 log loss 0.27165172868094783 in 1600\n",
      "Fold :  6\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.53128\tvalid_1's multi_logloss: 0.552849\n",
      "[200]\ttraining's multi_logloss: 0.36362\tvalid_1's multi_logloss: 0.392816\n",
      "[300]\ttraining's multi_logloss: 0.303852\tvalid_1's multi_logloss: 0.336454\n",
      "[400]\ttraining's multi_logloss: 0.280031\tvalid_1's multi_logloss: 0.313812\n",
      "[500]\ttraining's multi_logloss: 0.269603\tvalid_1's multi_logloss: 0.303716\n",
      "[600]\ttraining's multi_logloss: 0.264599\tvalid_1's multi_logloss: 0.298859\n",
      "[700]\ttraining's multi_logloss: 0.261976\tvalid_1's multi_logloss: 0.296278\n",
      "[800]\ttraining's multi_logloss: 0.260477\tvalid_1's multi_logloss: 0.294812\n",
      "[900]\ttraining's multi_logloss: 0.259554\tvalid_1's multi_logloss: 0.293959\n",
      "[1000]\ttraining's multi_logloss: 0.258954\tvalid_1's multi_logloss: 0.29346\n",
      "[1100]\ttraining's multi_logloss: 0.258546\tvalid_1's multi_logloss: 0.293156\n",
      "[1200]\ttraining's multi_logloss: 0.258253\tvalid_1's multi_logloss: 0.29297\n",
      "[1300]\ttraining's multi_logloss: 0.258029\tvalid_1's multi_logloss: 0.292845\n",
      "[1400]\ttraining's multi_logloss: 0.257849\tvalid_1's multi_logloss: 0.292761\n",
      "[1500]\ttraining's multi_logloss: 0.2577\tvalid_1's multi_logloss: 0.292702\n",
      "[1600]\ttraining's multi_logloss: 0.257555\tvalid_1's multi_logloss: 0.292676\n",
      "[1700]\ttraining's multi_logloss: 0.25742\tvalid_1's multi_logloss: 0.292651\n",
      "[1800]\ttraining's multi_logloss: 0.257282\tvalid_1's multi_logloss: 0.292629\n",
      "[1900]\ttraining's multi_logloss: 0.257147\tvalid_1's multi_logloss: 0.292608\n",
      "[2000]\ttraining's multi_logloss: 0.257014\tvalid_1's multi_logloss: 0.292586\n",
      "[2100]\ttraining's multi_logloss: 0.256887\tvalid_1's multi_logloss: 0.292588\n",
      "[2200]\ttraining's multi_logloss: 0.256761\tvalid_1's multi_logloss: 0.29257\n",
      "[2300]\ttraining's multi_logloss: 0.256639\tvalid_1's multi_logloss: 0.292577\n",
      "[2400]\ttraining's multi_logloss: 0.256514\tvalid_1's multi_logloss: 0.292571\n",
      "[2500]\ttraining's multi_logloss: 0.256396\tvalid_1's multi_logloss: 0.292583\n",
      "[2600]\ttraining's multi_logloss: 0.256283\tvalid_1's multi_logloss: 0.292605\n",
      "[2700]\ttraining's multi_logloss: 0.256166\tvalid_1's multi_logloss: 0.292609\n",
      "[2800]\ttraining's multi_logloss: 0.256055\tvalid_1's multi_logloss: 0.292618\n",
      "[2900]\ttraining's multi_logloss: 0.255949\tvalid_1's multi_logloss: 0.292618\n",
      "[3000]\ttraining's multi_logloss: 0.25584\tvalid_1's multi_logloss: 0.292629\n",
      "[3100]\ttraining's multi_logloss: 0.255731\tvalid_1's multi_logloss: 0.292619\n",
      "[3200]\ttraining's multi_logloss: 0.255629\tvalid_1's multi_logloss: 0.29263\n",
      "[3300]\ttraining's multi_logloss: 0.255526\tvalid_1's multi_logloss: 0.292638\n",
      "Early stopping, best iteration is:\n",
      "[2363]\ttraining's multi_logloss: 0.256559\tvalid_1's multi_logloss: 0.29256\n"
     ]
    },
    {
     "data": {
      "image/png": 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5GjBMxbIapyA1Tm6uj93tlLpDVuNUfl7M5cVMYLnCFUqucKubnn32WXr16sWq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\nVhoQ6ELda3EKatOBxsBKEXk/jPUmALm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xfQKf4+gF/hKfM6O+gO+HnAPApSXIuIiR1YNz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d760877b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 6 log loss 0.29256034268764847 in 2363\n",
      "Fold :  7\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.533759\tvalid_1's multi_logloss: 0.535758\n",
      "[200]\ttraining's multi_logloss: 0.366682\tvalid_1's multi_logloss: 0.368803\n",
      "[300]\ttraining's multi_logloss: 0.307068\tvalid_1's multi_logloss: 0.308751\n",
      "[400]\ttraining's multi_logloss: 0.283257\tvalid_1's multi_logloss: 0.28471\n",
      "[500]\ttraining's multi_logloss: 0.272809\tvalid_1's multi_logloss: 0.274143\n",
      "[600]\ttraining's multi_logloss: 0.267793\tvalid_1's multi_logloss: 0.269107\n",
      "[700]\ttraining's multi_logloss: 0.265141\tvalid_1's multi_logloss: 0.266536\n",
      "[800]\ttraining's multi_logloss: 0.263605\tvalid_1's multi_logloss: 0.265144\n",
      "[900]\ttraining's multi_logloss: 0.262654\tvalid_1's multi_logloss: 0.264366\n",
      "[1000]\ttraining's multi_logloss: 0.262036\tvalid_1's multi_logloss: 0.263916\n",
      "[1100]\ttraining's multi_logloss: 0.261609\tvalid_1's multi_logloss: 0.263652\n",
      "[1200]\ttraining's multi_logloss: 0.261305\tvalid_1's multi_logloss: 0.2635\n",
      "[1300]\ttraining's multi_logloss: 0.261073\tvalid_1's multi_logloss: 0.263415\n",
      "[1400]\ttraining's multi_logloss: 0.260884\tvalid_1's multi_logloss: 0.263379\n",
      "[1500]\ttraining's multi_logloss: 0.260725\tvalid_1's multi_logloss: 0.263368\n",
      "[1600]\ttraining's multi_logloss: 0.260569\tvalid_1's multi_logloss: 0.263354\n",
      "[1700]\ttraining's multi_logloss: 0.260426\tvalid_1's multi_logloss: 0.263352\n",
      "[1800]\ttraining's multi_logloss: 0.260284\tvalid_1's multi_logloss: 0.263356\n",
      "[1900]\ttraining's multi_logloss: 0.260152\tvalid_1's multi_logloss: 0.263352\n",
      "[2000]\ttraining's multi_logloss: 0.260018\tvalid_1's multi_logloss: 0.263375\n",
      "[2100]\ttraining's multi_logloss: 0.259883\tvalid_1's multi_logloss: 0.263383\n",
      "[2200]\ttraining's multi_logloss: 0.259752\tvalid_1's multi_logloss: 0.263382\n",
      "[2300]\ttraining's multi_logloss: 0.259627\tvalid_1's multi_logloss: 0.263386\n",
      "[2400]\ttraining's multi_logloss: 0.259501\tvalid_1's multi_logloss: 0.263399\n",
      "[2500]\ttraining's multi_logloss: 0.259379\tvalid_1's multi_logloss: 0.263409\n",
      "[2600]\ttraining's multi_logloss: 0.259269\tvalid_1's multi_logloss: 0.26341\n",
      "Early stopping, best iteration is:\n",
      "[1667]\ttraining's multi_logloss: 0.260473\tvalid_1's multi_logloss: 0.263345\n"
     ]
    },
    {
     "data": {
      "image/png": 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+teKcXyJ+4bd+wH89FWc/7777LqmpqcyaNSsY2zR69Gi6desGOLmCPXr0IBAI\nAPD999/z3//+l5Mnne/fv38/a9euJTs74gjSsLKzs4Pf4Qde9ePFoJUIrA2z/EYgCSd2qQawWkSW\nFWG/lYGAqo4UkTRgHNANSABSgQXueklAO5ybjDNFZIqqbs+7MxG5yH35NxFJBr4C7nHTOnJR1eeB\n5wEaNG6iE9f761Lhg61P4qee/NYP+K+n4uznycuO0K5du+Dpv127drFy5UqSk5PZt28fX375JSNH\njqRiReeW0LZt21KjRg2Sk5MBJ9m9V69e53ykFQgEgvv0A6/6KU3/Lw/GOAF7RCQnxmldhNvnjXE6\npqonRCRsjBOAiOTEOJ0xaOH82dQD/q2qfxKRPwETgFsKKiLugrJkju8ZYcmxIRAIsHVwstdlFBu/\n9QP+66k4+1m1alUwtikuLo709HTat3dyWefOncsNN9wQHLAAevXqxdSpUxk4cCCrVq2iatWqdmqw\nFPFiIsZG4LIwy6MW40TuwTnSGKd9wBGc628Ac4FfRVCjMaYUCY1tat26NadPn+YPf/gDAHPmzGHQ\noEG51u/RoweNGzemSZMm3HHHHTzzzDNelG3yYTFO+XCTO94Fkt2au+IE7BpjYky42CYg7DUZEWHa\ntGklUJU5GxbjVLCRwKsiMhn4Drgtyt9njDGmABbjVHCd3+Ac8RljjCkFLMbJGGNMzLBBCyfGSUQy\n8vy09rouY0z+wuUJDh48mObNm5OYmMiwYcM4ceIE4Fy7qlq1ajBr8JFHHvG4enO2bNAi/xgnr+sy\nxoSXkye4Zs0aNmzYwKlTp5gzZw6DBw9m8+bNrF+/nqNHjzJz5szgNp06dSIjI4OMjAzGjBnjYfXm\nXBR50BKRaiLS5ly+tLRlD+ZHRB4Vke1u2oYxphQJlyfYo0cPRAQR4YorrmDHjh1el2mKWUQTMUQk\ngBN/VA7IAL4TkaWq+qeifmGMZQ++C0wFsiLdwLIHSz+/9QP+66mgfraO70ndunULzBM8ceIEr776\naq6nDq9YsYK2bdtSp04dJkyYQKtWraLehyl+8vP9uAWsJPJfVW0nIr8H6qvqWBFZp6pFPuISkV8D\nKap6dZ7lgpMVmCt7MORR94keZA/m1JYdOjsxzOeh2YOXjZk8I9I/jphQKw72HPW6iuLjt37Afz0V\n1E/rulU5dOgQY8eOZcyYMcE8wc6dOwfzBCdMmEDFihW55557ACeKqUyZMsTFxbFy5UqmTp3KrFmz\nSqodwMnqi4/P99dIzCnufrp06bJWVdsXuqKqFvqDc19UbeAj4HJ32bpItg2zr3uBSWGW9wMWAWVx\njrq2ud/ZCNjgrjMUmBqyzXtcNqcdAAAgAElEQVRAsvtage7u6zS31gtwsgwzQrbfAlTFuWn5G5xB\nuLCasyPtr1mzZuo3S5Ys8bqEYuW3flT911Nh/bz55ps6bNiw4PvU1FS96667VFU1JSVFe/furadO\nncp3+4YNG+p3331XLLVG6nz7OyoqYI1G8Ds20mtajwAfAl+p6moRaUwRTplFKJg9qE4obU72YKTy\nZg8uVdUT7utGIeulq+pBVf0JJ+Gi4TlXbowpUQ0aNAjmCaoq6enptGzZkpkzZ/Lhhx/y+uuvU6bM\nz7/evv3225x/gPLpp59y+vRpfvGLX3hVvjkHEV3TUtW5ONl7Oe+34BwZnY2NQP8wy6OWPSgiZ5M9\naIwppULzBMuVK0e7du34wx/+QOXKlWnYsCFXXXUVADfeeCNjxozhrbfe4tlnn6VcuXLExcUxZ84c\nnCsSJtZEOhGjGfAsUEuda0ttgF6qOu4svjMmsgeNMaVbuDzBnGdg5XXPPfcEr2+Z2Bbp6cEZwJ+B\nEwCqug4YeDZf6B4N9QW6uVPeNwIpwGycx5B8hjOwjVDVb/NsHpo9OIEoZw+KyBMisgOoJCI7RCQl\nmt9njDGmYJGeGqukqp/mOZw+68eKauxkD44ACp1daIwxpmREeqT1vYhcijNDDxHpD+yOWlXGGBMi\nXGTT119/zZVXXknTpk256aabOH78OADbtm2jS5cutGvXjjZt2rBw4UKPqzfFKdJB627gOaCFiOzE\neYzInVGrqoRZ9qAxpVd+kU0jR47kgQceICsri2rVqvHCCy8AMG7cOAYMGMB///tf5syZw/Dhwz3u\nwBSnQgctd9JDe1W9BrgYaKGqHdV5bMdZKW0xTppP9qCI3CQi60Rko4g8Ec0ajDH5yxvZVLt2bRYv\nXkz//s5E5CFDhjBv3jzAeYjjjz/+CMDBgwepU6eOZ3Wb4lfoNS13yvg9wJuqevhcvzBWYpxE5BfA\nk8BlqvqdiKSKSFdVTS9oO4txKv381g/4r6ecfvKLbLrsssu46KKLKFfO+RVWr149du7cCUBKSgrX\nXnstU6ZM4fDhw3z88cdetmKKWaQTMRaJyEPAG0Bw4FLVH87iO7vg3FM1PWQ/GeJ4kjwxTqEblnCM\nU2PgC1X9zn3/Mc69aWcMWnlinBjT+qznqJRKteKcXyJ+4bd+wH895fQTCAQ4dOgQqampzJo1KxjZ\n9I9//IOjR48SCAQA2Lt3L0eOHCEQCPDmm2/SqVMnBgwYwMaNG+nXrx8vvvhirpuNvZCdnR2s1w+8\n6ifSQWuY+793hyxTnF/sRZUIrA2z/EYgCSd2qQawWkSWFWG/lYGAqo4UkTRgHNANSABSgQXueklA\nO5ybjDNFZIqqbg+zvy9xruE1AnYAfYDy4b5YVZ8Hngdo0LiJTlzvr/uVH2x9Ej/15Ld+wH895fSz\ndXAyc+fOpV27dvTp0weAXbt2sWLFCo4dO0bHjh0pV64cK1asoGnTpiQnJ3P33XfzwQcfUL9+fZKT\nk5k4cSKJiYnUrFnT054CgQDJycme1lCcvOon0kSMX0a7EEJinIA9IpIT47Quwu3zxjgdU9UTIhI2\nxglARHJinM4YtFR1v4jchXN0eRr4hAgG6bgLypI5vmeEJceGQCDA1sHJXpdRbPzWD/ivp9B+QiOb\n4uLiSE9Pp3379nTp0oW33nqLgQMHkpqaSu/evYPrp6enM3ToUD7//HN++uknLr74Yg+7McUp0kSM\nW8MtV9VXzuI7YybGSVXfxXk8Sc4pwFMR1GiMKUb5RTb17NmTgQMH8pe//IV27dpx++23AzBx4kTu\nuOMOJk2ahIjw8ssvW2STj0R6PiE0uLYi0BUnjeJsBq2YiXESkZqquldEqgHDCX9DtDEmysJFNjVu\n3JhPP/30jHUTEhL497//XVKlmRIW6enBP4a+F5GqwKtn84WqqiLSF5gsIqOAn3AGo/uBeJwYJ8WN\ncXKvKeUIjXHaQJRjnICnRKSt+/oRVS01sxuNMeZ8dLZXbo8ATc/2S2MoxmlQQZ8bY4wpWZFe03oX\nN8IJ55pSAiGPKjHGGGNKQqRHWhNCXp8EvlHVHVGoxxMisgqokGfxLaq63ot6jDlfZWZmctNNNwUf\n5b5lyxYeeeQRkpOTufPOO/npp58oV64czzzzDFdccQX79+9n2LBhfPXVV1SsWJEXX3yRxMREr9sw\nURTpoNVDVUeGLhCRx/Mui1WqeqXXNRhjoHnz5mRkZBAIBOjUqRN169alb9++3HHHHYwdO5bu3buz\ncOFCRowYQSAQ4O9//ztJSUmkpaWxefNm7r77btLTCwytMTEu0lvEu4VZ1r2wjUpbxmB+RORREdnu\npmqELr9TRNa7Abr/EpEEL+oz5nyUnp7OpZdeSsOGDfPNE9y0aRNdu3YFoEWLFmzdupU9e/Z4VrOJ\nvgKPtNyba4cDjUUk9CbfC3Fm8hW0bUxkDLreBaYCWXmWz86JmxKRXsA/gOsL2pFlD5Z+fusH/NHT\n1jw35c+ZM4dBg5y5UJMnT+a6667joYce4vTp03zyyScAtG3blnfeeYeOHTvy6aef8s0337Bjxw5q\n1apV4vWbkiE/348b5kNnans14DFgVMhHhwrLHRSRXwMpqnp1nuWCkwmYK2PQndr+nqomlnDGYGht\n2aGzEPN8Ngi4VVXPOMLMkz142ZjJMwr7qphSKw72HPW6iuLjt37AHz21rls1+Hr//v0MHTqUl156\nierVq/P000/Ttm1bOnfuzJIlS3jvvfeYOHEihw8fZurUqWRlZdG4cWO2bdvGQw89RJMmTTzsJLyc\n63R+Udz9dOnSZa2qti90RVWN+AeoiTM4NAAaFLLuvcCkMMv7AYuAsjhHXduA2jhRSxvcdYYCU0O2\neQ9Idl8r0N19nQZ8BFyAk1mYEbL9FqAqzs3J3wD1I+gvO8yyu4GvcKKemha2j2bNmqnfLFmyxOsS\nipXf+lH1X09/+9vftFu3bsH3VapU0dOnT6uq6unTp/XCCy88Y5vTp09rw4YN9eDBgyVWZ1H47e+o\nuPsB1mgE41BE17RE5DcikoVzY+9SnJuB349k2zCCGYOqusfd3+WFbBMqb8bgUlU94b5uFLJeuqoe\nVNWfgJyMwSJT1WmqeikwEvjL2ezDGFM0ixcvDp4aBKhTpw5Lly4Nfta0qXOb6IEDB4JPLJ45cyZX\nX301VapUKfmCTYmJdPbgOKAD8LGqthORLkBhN97GTMZghOYAz57jPowxhThy5Ahr164lLS0tuGzG\njBncd999nDx5kooVK/L8888D8Pnnn3PrrbdStmxZEhISgk8vNv4V6S/yE6q6T0TKiEgZVV0iIo8X\nsk3MZAzmR0SaqmrO5IyenDlRwxhTzCpVqsT8+fOpWvXna1wdO3Zk7dozn2h01VVXkZVl/1meTyId\ntA6ISDywHHhNRPbiHA3lSzV2MgZF5AngZqCSiOwAZqoTBXWPiFwDnMAZbIdEsw5jjDEFi3TQ6g0c\nxRlwBuNMcHiksI00djIGRwBnzC5U1fsK2s4YY0zJimgihqoeBurjzOBLBWbiTIgwxphik5mZSVJS\nEr///e9JSkqiSpUqTJ48mYyMDDp06EBSUhLt27cPPpLkySefJCkpiaSkJBITEylbtiw//FDg3Tgm\nxkU6e/AO4C3gOXdRXWBetIqKFhFZ5aZbhP609rouY4wjJ8Zp5syZrF27lkqVKtG3b19GjBjB2LFj\nycjI4JFHHmHECOfEyMMPP0xGRgYZGRk89thjdO7cmerVq3vchYmmSE8P3o0zGWIVgKpmiUjNwjYS\nkUuAyThT2o/hXtPSMM+lCr25OMKaikzzyRgUkUeBW4FqoacfRWQS0MV9WwmoqaoXRas+Y8zPIolx\nCvX666/nmiZv/CnSQeuYqh7PeWS1O7U8/ygN/BHjpKoP5LwWkT8C7QrbkcU4lX5+6wf80dPZxDjl\nOHLkCB988AFTp04tsXqNNyIdtJaKyP8CcSLSDSeP8N1CtumCM1V+es4CVc0Qx5PkiXEK3bCkY5xU\ndaX7PQX1MwgYG+6DPDFOjGld4MTKmFMrzvml6Bd+6wf80VMgEAi+3r9/P2+//TY33HADgUCAp59+\nmttvvz0Y43TjjTcyceLE4PqLFy+mRYsWrFu3LsyeS4fs7OxcPcY6z/qJJDYD59rXHTgPfnzLfS2F\nbOOLGCd3eUNgN1C2sH1YjFPp57d+VP3XU1FjnPr06aOvvfZaidZYVH77OyqVMU4i0sAd2E6r6gxV\n/a2q9ndfF3h6sAAxFePkGgi8paqnzmEfxpgIRRrjBM41rqVLl9K7d+8Sr9OUvMJOD84DfgUgIm+r\nar8i7NtPMU4DcSajGGOirCgxTgBpaWlce+21VK5c2YtyTQkr7Bd56ADTuIj7jvkYJwARaY7zeJYV\nXtVgzPmkKDFOAEOHDmXo0KElVJ3xWmH3aWk+rwvlHg31Bbq5Ty7eCKQAs4F1ODFOi3FjnPJsHhrj\nNIESiHFy45sqicgOEUkJ+XgQMOccTocaY4wpJoUdabUVkR9xjrji3Ne471VVC3wGgMZ4jFO47zDG\nGOOdAgctVS1bUoUYY4wxhTnXZ0zFFBFZBVTIs/gWVV3vRT3GGCdv8Kabbgq+37JlC7feeivPPvss\nmZmZgPOwx4suuoiMjAy2bt1Ky5Ytad68OQAdOnRg+vTpYfdt/MeTQcureCfNJ8apgDo/wLmHrBzO\nY1nutmnvxhSvnLxBgFOnTlG3bl06duzIwIEDg+s8+OCDuSZmXHrppcFtzPklosDc4hQS7xRQ1UtV\nNQEn1aJWSdcSgQGq2hbnWtvFwG89rscYX8vJG7zkkkuCy1SVN99803IFDeDNkVYsxTvlTDwpB5Qn\nghmUlj1Y+vmtH4jdngrKG8yxfPlyatWqleuG4q+//pp27dpRpUoVxo0bR6dOnUqkXuM9LwatRCDc\nDRc3Akk4cUw1gNUisqwI+62Mc/Q2UkTSgHFANyABSAUWuOsl4QTfHgMyRWSKqm7Pb6ci8iHOfWLv\n40RYhVvHsgdjiN/6gdjtKTS77sSJE8G8wdBcu0mTJnHFFVcE3x8/fpzZs2dTtWpVMjMz6devHy+9\n9FKpv7nYsgeLSSRZT8X5Q/6ZhJOAYSHvX8U5KmpEZJmEx3DzEHGeqjxaf85NPBCy/YyQ7d8HOkZQ\nc0XgbaBbYeta9mDp57d+VP3R07x584J5gzn9nDhxQmvWrKnbt2/Pd7vOnTvr6tWrS6LEc+KHv6NQ\npTJ7MEo2ApeFWR61eCdyH1EWOd5JnfzCBYCFmxkTJeGeh/Xxxx/TokUL6tWrF1z23XffceqUMx9q\ny5YtZGVl0bhxUQN7TKzyYtBaDFRwn4YMnBHvVFZELsaJd/o0z7ZbgSQRKSMi9YlivJOIxItIbfd1\nOaAHsDla32fM+ezIkSMsWrSIG2+8MdfycNe4li1bRps2bWjbti39+/dn+vTp9rTi80iJX9NSVRWR\nvsBkERkF/IQ75R2Ix4l3Utx4J3fKe47QeKcNRDfeqTKwQEQq4DxGZTFgN4MYEwWVKlVi3759Zyx/\n+eWXz1jWr18/+vUrSna38RNP7tPSGIh3UuexKUV5ZIoxxpgo8+L0oDHGGHNWbNDCiXcSkYw8P629\nrssYP8vMzCQpKSn4U6VKFSZPngzAO++8Q/PmzWnVqhUjRji3Ur722mu51i9TpoylYpyHzqvswfxo\nEeOdjDHnLlx8U9++fVmyZAn//ve/WbduHRUqVGDv3r0ADB48mMGDnasD69evp3fv3iQlJXlWv/FG\nVI+0ROQSEZnjPk9rk4gsFJFm+azbSEQ2RLOe/IjIoyKy3U3VCF3+J7fudSKSLiINvajPGL/LiW9q\n2LAhzz77LDfffDMVKjjZ1jVr1jxj/XDT4835IWpHWiEZg6mqOtBdloSTMXhGMK7H3gWmAll5lv8X\nJzbqiIjchRMLdVPejUNZjFPp57d+ILZ6yhvdBLmntn/xxRfExcVx5ZVXUrFiRSZMmMDll+eeE/XG\nG28wf/78EqnXlC7RPD0YSxmDK93vybt8ScjblcDvwm1vMU6xxW/9QGz1lDf6JzS+KRAIcPDgQX74\n4QfGjx/P5s2b6dWrF7Nnzw7+97lp0yZUle+//z6mYpEsxqmYRBKbcTY/5B/X1A9YhHPvUy1gG87j\nPxoRWVyTAt3d12nAR8AFOJmFGSHbbwGq4qRmfAPUj6Dm7AI+mwr8pbB9WIxT6ee3flRju6fQ+CZV\n1euuu04nTZoUfN+4cWPdu3dv8P3999+vjz76aInWWBxi+e8oHK9inLyYiNEReF2d51LtEZGlOPdD\nrYtw++PAB+7r9cAxVT0hIutxBr4c6ap6EEBENgENgXyDcQsiIr8D2gOdz2Z7Y0z+8l6f6tOnD598\n8gngnCo8fvw4NWrUAOD06dPMnTuXZcuKkqVt/CSaEzFiLmMwHBG5BhgN9FLVY4Wtb4yJXLj4pmHD\nhrF7924SExMZOHAgqampwVODy5Yto169epY1eB6L5qAVExmDBRGRdsBzOAPWXi9qMMbPcuKbQp9K\nXL58eUaPHs2GDRv4z3/+w69//evgZ8nJyaxcudKLUk0pEbVByz0a6gt0c6e8bwRSgNk4pwI/wxnY\nRqjqt3k2D80YnEB0MwYRkSdEZAdQSUR2iEiK+9GTOHmIc90bjhfkuxNjjDFRF9VrWhoDGYPu5yOA\nM2YXquo1BW1njDGmZFmMkzGmxBUU4TRlyhRuvfXWXBFOObZt20Z8fDwTJkzwomxTCpxXMU4isgqo\nkGfxLaq63ot6jDlfFRThNH/+fGbOnMm1114bjHDK8cADD9C9e3cvSjalxHkV46SqV6pqUp6f9fnF\nOIXU1l9EVETaR7M+Y85HeSOcRo0aRfny5YHcEU7z5s2jcePGtGrVyqtSTSlgMU6O/GKcEJELcW6U\nXhXJjizGqfTzWz8QWz3ljXHKG+G0fPly3nzzTWrWrBmMcDp8+DCPP/44ixYtslOD57loHmmFjXEC\n/iUiT4rIBhFZLyJnZPmJyFARmRry/j0RSXZfZ4vI4yKyVkQ+FpErRCQgIltEpFfI9u+IyAcikiUi\nTxRUqKquVNXd+Xz8N5yYqJ+K+gdgjCnY8ePHWbBgAb/97W8BOHnyJPv37+eZZ57hySefZMCAAagq\nY8eO5YEHHiA+Pr6QPRq/i+Y1rURgbZjlNwJJOLFLNYDVIlKU29srAwFVHSkiacA4oBuQAKQCOdPS\nk4B2ODcZZ4rIFFUtUiKGe59WfVV9T0QeKmA9yx6MIX7rB2Krp9C8un/961/88pe/5PPPP+fzzz+n\nUqVKNG7cmMOHDyMiHD9+nPnz5/PRRx8xa9Ys7r33XrKzsylTpgzbt2+nb9++3jVSRJY9WDwsxikf\nIlIGmISTY1ggVX0eeB6gefPm+sfBvSP9mpgQCAQYkJzsdRnFxm/9QOz2NH36dIYPH06yW/uwYcPY\ntWsX8fHx1KlThzJlytC7d2/69OkT3CYlJYX4+Hgeeijff0eWSoFAINinH3jVj8U45e9CnKPFgIhs\nBToAC2wyhjHFI78Ipy1btnDbbbedEeFkDET3SGsx8HcRuUNVZ8AZMU6pQHWcGKeHyT0wbQWGu0c7\ndfEgxsk9SquR815EAsBDqrqmpGsxxo9yIpxClS9fnlmzZhX4r/iUlJToF2dKLYtxosAYJ2OMMaWI\nxTiRf4xTnnWSC/rcGGNM9FmMkzHGmJhxXg1aIrLKTWsP/WntdV3GxLr8sgTnzp1Lq1atKFOmDGvW\n/Hw5+MSJEwwZMoTWrVvTsmVLHnvsMQ+rN7HkvMoeVNUrva7BGD/KL0vwyJEjvPPOO/zP//xPrvXn\nzp3LsWPHWL9+PUeOHCEhIYFBgwbRqFEjD6o3scSTQUtELgEm49yfdQxntuD9qnpGvJOINALeU9XE\nEiwxJ75peciiesAsVb2/JOswJtaEZgnmR0Q4fPgwJ0+e5OjRo5QvX54qVaqUYJUmVpX4oBUrmYSq\neggnVQMAEVkLvFPYdpY9WPr5rR/wtqeCsgTz079/f+bPn0/t2rU5cuQIkyZNonr16tEs0/iEF0da\nYTMJxfEk0B1QYJyqvhG6oYgMBdqr6j3u+/eACaoacBPapwHX4NwL9r84mYENcI7iFrjb9wIqAZcC\nae7MwQKJSFOgJrmPvEI/txinGOK3fsDbnkKjfE6cOMHbb7/NDTfckGv5gQMHWLt2LdnZzoMU1q9f\nz/fff8/rr7/OoUOHuO+++4IpGOC/yCPwX0+e9aOqJfqDk5g+KczyfsAioCzOUdc2oDZONNMGd52h\nwNSQbd4Dkt3XCnR3X6cBHwEX4GQcZoRsvwWoinMz8zc42YKF1TwGZ3AstL9mzZqp3yxZssTrEoqV\n3/pRLT09zZs3T7t163bG8s6dO+vq1auD74cPH66vvPJK8P1tt92mb7zxRvB9aemnOPmtp+LuB1ij\nEfyOLU2zB4OZhKq6B8jJJIxU3kzCpap6wn3dKGS9dFU9qKo/ATmZhIUZCLxehFqMOS+9/vrrhZ4a\nBGjQoAGLFy9GVTl8+DArV66kRYsWJVChiXVeDFoxlUkoIm2BcqoaLrHeGOMKlyWYlpZGvXr1WLFi\nBT179uS6664D4O677yY7O5vExEQuv/xybrvtNtq0aeNV6SaGeHFNK9YyCQdhR1nGFCpclmDfvn3D\nPj4kPj6euXPnllRpxkdKfNBSVRWRvsBkERmF83DFrcD9QDxOJqHiZhK6U95zhGYSbiDKmYSuAUCP\nEvgeY4wxhfDkPi2NkUxCd53Gha1jjDGmZJSmiRjGGA8dOHCA/v3706JFC1q2bMmKFStISUmhbt26\nwXimhQsXAhbDZLxzXsU45UdEVgEV8iy+RVXXe1GPMV647777uP7663nrrbc4fvw4R44c4cMPP+SB\nBx444ynBFsNkvGIxToTPJBSRSiLyT5ybkE8B76rqqGjVYIyXfvzxR5YtW8bLL78MOA9jLF++fL7r\nWwyT8YrFOBVsgqouEZHyQLqIdFfV9wvawGKcSj+/9QPn1tPW8T3ZsmULF198MbfddhufffYZl112\nGU899RQAU6dO5ZVXXqF9+/ZMnDiRatWqWQyT8Yz8fGtTCX2hyK+BFFW9Os9ywYldyhXjFHqk5VWM\nk/tdT+Ekc8wI81lojNNlYyafsUpMqxUHe456XUXx8Vs/cG49ta5blczMTIYPH86UKVNISEhgypQp\nVK5cmT59+lC1alVEhBdffJF9+/YxcuRI1q9fz/z58xk1alQwhmn8+PHBGKZzlZ2dTXx8fOErxhC/\n9VTc/XTp0mWtqrYvbD0vTg8mAuFu1L0RJ6C2LVADWC0iy4qw38pAQFVHikgaMA7oBiQAqcACd70k\noB3OaclMEZmiqtsL2rGIXAT8Bngq3Oeq+jzwPECDxk104np/XSp8sPVJ/NST3/qBc+tp6+BkWrRo\nwWOPPcbw4cMBKFu2LOPHj891o3Djxo254YYbSE5OZu7cuQwZMoRrrrkGgHfffZdy5cqRnJx8zr2A\nk2dYXPsqLfzWk1f9lKb/coMxTsAeEcmJcVoX4fZ5Y5yOqeoJEQkb4wQgIjkxTvkOWiJSDufm4qdV\ndUthRcRdUJbMPKnXsS4QCLB1cLLXZRQbv/UD597TJZdcQv369cnMzKR58+akp6eTkJDA7t27qV27\nNuCkWyQmOpeWc2KYfve733HkyBFWrlzJ/ffbU3tM9HkxaG0E+odZHrUYJ3fgyVGkGCecI6gsVZ0c\nQX3GxKwpU6YwePBgjh8/TuPGjXnppZe49957ycjIQERo1KgRzz33HODEMN12220kJiaiqhbDZEqM\nxTgVQETG4STC/z6a32NMaZCUlMSaNWtyLXv11VfDrmsxTMYrFuOUDxGpB4wGNgP/ceaJMFVVZ0br\nO40xxhTMYpzyr3EHkZ2yNMYYU0IsxskYY0zMsEELJ8ZJRDLy/LT2ui5jikO4TMGHH36YFi1a0KZN\nG/r27cuBAweC6z/22GM0adKE5s2b8+GHH3pYuTFnskELJ8ZJVZPy/FjuoPGFnEzBzZs389lnn9Gy\nZUu6devGhg0bWLduHc2aNQsG3m7atIk5c+awceNGPvjgA4YPH86pU6c87sCYn3kyaInIJSIyR0S+\nEpFNIrJQRJrls24jEdlQ0jW63x0QkcyQo6+aXtRhzNnKyRS8/fbbASdT8KKLLuLaa6+lXDnnknaH\nDh3YsWMHAPPnz2fgwIFUqFCBX/7ylzRp0oRPP/3Us/qNycuyBws3WFXXFL6aw7IHSz+/9QPheyoo\nU7By5crB9V588UVuuukmAHbu3EmHDh2Cn9WrV4+dO3eWTBPGRMCL2YNdcG4Enp6zQFUzxPEkebIH\nQzf0MnuwIHmyBxnT+uS57rJUqRXn/FL0C7/1A+F7CgQCZGZmsnbtWoYOHcrQoUOZMmUKd911F8OG\nDQNg1qxZHDhwgLp16xIIBNixYweff/45gUAAgN27d7Nx40Zq1KhRov1kZ2cHa/ALv/XkWT+qWqI/\nwL3ApDDL+wGLgLI4R13bgNo4EUwb3HWG4twrlbPNe0Cy+1qB7u7rNOAj4AKcLMOMkO234NwwXBH4\nBqhfQK0BnHvCMoD/ww0YLuinWbNm6jdLlizxuoRi5bd+VPPvaffu3dqwYcPg+2XLlmmPHj1UVfXl\nl1/WDh066OHDh4Of//3vf9e///3vwffXXnutfvLJJ1GpuSDn099RrCrufoA1GsEYUpomYgSzB1V1\nD5CTPRipvNmDS1X1hPu6Uch66ap6UFV/AnKyB/MzWFVbA53cn1uKUI8xngvNFASCmYIffPABjz/+\nOAsWLKBSpUrB9Xv16sWcOXM4duwYX3/9NVlZWVxxRVSDZ4wpEsseLODPQFV3uv97SERm48RGvRJB\nncaUGuEyBS+//HKOHTtGt27dAGcyxvTp02nVqhUDBgwgISGBcuXKMW3aNMqWLetxB8b8zLIH8+EO\ndBep6vcicgFwA/BxtHgOBiUAAAxTSURBVL7PmGgJlyn45Zdf5rv+6NGjGT16dLTLMuasWPZg/ioA\nH7oDVlmcActfT3c0xpgYY9mD+dd4GLisgDaMMcaUsNI0EcMYc47CRTb98MMPdOvWjaZNm9KtWzf2\n798PODOH7733Xpo0aUKbNm34z3+ieeLCmOJhgxaWPWj8I1xk0/jx4+natStZWVl07dqV8ePHA/D+\n+++TlZVFVlYWzz//PHfddZfH1RtTuKgOWrES14RzveoXQBMNyR4UkatF5D8iclJEws14NKbUyC+y\naf78+QwZMgSAIUOGMG/ePMCJbLr11lsRETp06MCBAwfYvXu3Z/UbE4moXdOKsbimd4GpQFae5dtw\nbkh+KNIdWYxT6ee3fgBevr5yvpFNe/bsoXbt2gDUrl2bvXv3Ak5kU/369YP7yIlsylnXmNIomhMx\nYiauSVVXut+Td/lWd/npghq1GKfY4rd+wInUyS+y6eTJk7nidnLef//99/z3v//l5Ennz2L//v2s\nXbuW7Oxsj7r4md8ij8B/PXnVTzQHrURgbZjlNwJJOPFKNYDVIrKsCPutDARUdaSIpAHjgG5AApAK\nLHDXSwLa4dxMnCkiU1R1+1l1UghVfR54HqBB4yY6cb0nkzKj5sHWJ/FTT37rB5wjrd69e/PYY48x\nfPhwAMqWLcv48eOpW7cuzZs3p3bt2uzevZs6deqQnJxM27ZtqVGjBsnJyQAcPnyYXr16lYojrUAg\nEKzLL/zWk1f9ePFfbjCuCdgjIjlxTesi3D5vXNMxVT0hImHjmgBEJCeuKSqDVqi4C8qSOb5ntL+m\nRAUCAbYOTva6jGLjt37A6Sk0sql58+bByKaEhARSU1MZNWoUqamp9O7dG3Aim6ZOncrAgQNZtWoV\nVatWLRUDljEFieagFTNxTcb4RbjIptOnTzNgwABeeOEFGjRowNy5cwHo0aMHCxcupEmTJlSqVImX\nXnrJ4+qNKVw0f5HHRFyTMX4SLrIJnKDcvESEadOmlURZxhSbqE15d4+G+gLd3CnvG4EUYDbOqcDP\ncAa2Ear6bZ7NQ+OaJhDduCZE5AkR2QFUEpEdIpLiLv9/7Z1/rJZlGcc/XyEgBDmg5Cic/BjVTlZI\nNMF+SGUozqFtsGHMIO3HdGv9WBGMcos5p7Eac5TgKHHFFEE0YktypCxzEaD8OAjISSgoEs1Cpdb4\ncfXHfb2H5xze8/s9PO/znuuzPXvv53ru532u73ud977Pcz/3e18fdftMYLlrCIIgCHKiR4fMirBc\nkx+fB5wzu9DMtgIj2zo3CIIgOH/EihhBEARBYehVkxMkbSGt3p7lVjPbnYc/QRAEQefoVZ2WmV2V\ntw9BEARB14nhwSAIgqAwRKcVBEEQFAad/Z1uUAkkvQXsz9uPCnMJ8HreTlSQWtMDtaep1vRA7Wmq\ntJ7LzWx4e5V61TOt88R+M5uYtxOVRNK2WtJUa3qg9jTVmh6oPU156YnhwSAIgqAwRKcVBEEQFIbo\ntCrPg3k70APUmqZa0wO1p6nW9EDtacpFT0zECIIgCApD3GkFQRAEhSE6rSAIgqAwRKdVQSRdL2m/\npEZJ8/P2pyNIukzSM5L2Stoj6etuHybpaUkH/HWo2yXpfte4S9KEfBWUR1IfSS9K2uD7oyVtcT2r\nJfVze3/fb/Tjo/L0uzUk1UlaK2mfx2pykWMk6Zv+99Yg6RFJA4oWI0k/l3RMUkPG1umYSJrj9Q9I\nmpOHlowv5TQt9r+7XZKekFSXObbANe2XdF3G3nNtoZnFVoEN6AP8GRgD9CPlC6vP268O+D0CmODl\nwcDLQD3wQ2C+2+cD93n5BuA3pAzUk4AteWtoRde3SLnbNvj+Y8AsLy8D7vDyncAyL88CVufteyt6\nHga+5OV+QF1RY0RK7HoQeGcmNnOLFiNSAtsJQEPG1qmYkBLhvuKvQ708tMo0TQX6evm+jKZ6b+f6\nA6O9/evT021h7oGvlQ2YDGzM7C8AFuTtVxd0/Ar4LGlVjxFuG0H60TTAcuCWTP2metWykXKgbQI+\nDWzwhuL1zBevKVbARmCyl/t6PeWtoYWei7yRVwt7IWPkndZhb6j7eoyuK2KMgFEtGvhOxQS4BVie\nsTerVw2aWhz7HLDKy83auFKcerotjOHBylH6IpY44rbC4MMuVwJbgEvN7CiAv77LqxVB5xJSUs8z\nvn8x8G8zO+X7WZ+b9Pjx416/mhgDvAY85EOeKyRdSEFjZGZ/I2Uk/ytwlPSZb6fYMSrR2ZhUdazK\ncBvpjhFy0hSdVuVQGVthfk8gaRDwOPANM3uzraplbFWjU9KNwDEz2541l6lqHThWLfQlDdk8YGZX\nAidIQ0+tUdWa/DnPTaQhpXcDFwLTylQtUozaozUNhdEmaSFwClhVMpWp1uOaotOqHEeAyzL7I4G/\n5+RLp5D0DlKHtcrM1rn5VUkj/PgI4Jjbq13nx4Dpkg4Bj5KGCJcAdZJKa21mfW7S48eHAG+cT4c7\nwBHgiJlt8f21pE6sqDG6FjhoZq+Z2UlgHXA1xY5Ric7GpNpjBaTJIsCNwGzzMT9y0hSdVuXYCozz\nGVD9SA+M1+fsU7tIEvAzYK+Z/ThzaD1Qmsk0h/Ssq2T/gs+GmgQcLw2HVANmtsDMRprZKFIMfmdm\ns4FngBleraWeks4ZXr+q/tM1s38AhyW9z02fAV6ioDEiDQtOkjTQ//5KegobowydjclGYKqkoX4H\nOtVtVYOk64HvAtPN7D+ZQ+uBWT67czQwDvgTPd0W5vnAr9Y20gyhl0kzZxbm7U8Hff446dZ9F7DD\ntxtIzww2AQf8dZjXF/AT17gbmJi3hja0TeHs7MEx/oVqBNYA/d0+wPcb/fiYvP1uRct4YJvH6UnS\nTLPCxgj4AbAPaAB+QZqBVqgYAY+QnsmdJN1d3N6VmJCeEzX69sUq1NRIekZVah+WZeovdE37gWkZ\ne4+1hbGMUxAEQVAYYngwCIIgKAzRaQVBEASFITqtIAiCoDBEpxUEQRAUhui0giAIgsIQnVYQdBBJ\npyXtyGyjuvAedZLurLx3Te8/veKrard/zZsl1Z/Pawa9l5jyHgQdRNLbZjaom+8xivTbsSs6eV4f\nMzvdnWv3BL5CxQqSprV5+xPUPnGnFQTdQClv12JJWz3f0FfdPkjSJkkvSNot6SY/5V5grN+pLZY0\nRZ7zy89bKmmulw9JukvSc8BMSWMlPSVpu6TfS3p/GX/mSlrq5ZWSHlDKl/aKpGs8X9JeSSsz57wt\n6Ufu6yZJw90+XtIfM3mUSrmhnpV0j6TN+EoJwGLXNFbSl/3z2CnpcUkDM/7cL+l592dGxod5/jnt\nlHSv29rVG/RC8v5VeWyxFWUDTnN2VYAn3PYV4Hte7k9atWI0aZHbi9x+CWlVAXFuKosp+Kodvr8U\nmOvlQ8C8zLFNwDgvX0Vazqilj3OBpV5eSVp/UaQFat8EPkj6Z3U7MN7rGWlNOYC7MufvAq7x8iJg\niZefBX6aueZKYEZm/+JM+W7ga5l6a/z69UCj26cBzwMDfX9YR/XG1vu20uKUQRC0z3/NbHwL21Tg\nQ5m7hiGkNdiOAPdI+iQpRcp7gEu7cM3V0LQK/9XAmrRcH5A6yfb4tZmZpN3Aq2a2299vD6kD3eH+\nrfb6vwTWSRoC1JnZZrc/TOpwmvnVCldIupuUqHIQzdfSe9LMzgAvSSp9HtcCD5mva2dmb3RDb1Dj\nRKcVBN1DpDuJZouc+hDfcOAjZnZSadX5AWXOP0XzYfqWdU746wWkfFMtO832+J+/nsmUS/utff87\n8qD7RBvHVgI3m9lO/xymlPEHzqawUJlrdlVvUOPEM60g6B4bgTuU0rsg6b1KCRqHkPJ6nZT0KeBy\nr/8WMDhz/l+Ael8pewhpxfNzsJTj7KCkmX4dSfpwhTRcwNnV1T8PPGdmx4F/SfqE228FNpc7mXM1\nDQaO+mcyuwPX/y1wW+bZ17Ae1hsUmOi0gqB7rCCl1XhBUgMpXXpfUqK8iZK2kRrufQBm9k/gD5Ia\nJC02s8PAY6TnR6uAF9u41mzgdkk7gT2k51SV4ATwAUnbSfnHFrl9DmmCxS7SKvOLWjn/UeA7SlmV\nxwLfJ2W/fhrX3RZm9hQpdcU2STuAb/uhntIbFJiY8h4EvZxKTOUPgvNF3GkFQRAEhSHutIIgCILC\nEHdaQRAEQWGITisIgiAoDNFpBUEQBIUhOq0gCIKgMESnFQRBEBSG/wMU3L/FOWcLGgAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d7614f940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 7 log loss 0.263345294966396 in 1667\n",
      "Fold :  8\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.533883\tvalid_1's multi_logloss: 0.535667\n",
      "[200]\ttraining's multi_logloss: 0.366833\tvalid_1's multi_logloss: 0.368266\n",
      "[300]\ttraining's multi_logloss: 0.307135\tvalid_1's multi_logloss: 0.308266\n",
      "[400]\ttraining's multi_logloss: 0.283279\tvalid_1's multi_logloss: 0.28444\n",
      "[500]\ttraining's multi_logloss: 0.272811\tvalid_1's multi_logloss: 0.27416\n",
      "[600]\ttraining's multi_logloss: 0.267776\tvalid_1's multi_logloss: 0.269307\n",
      "[700]\ttraining's multi_logloss: 0.265121\tvalid_1's multi_logloss: 0.266833\n",
      "[800]\ttraining's multi_logloss: 0.263613\tvalid_1's multi_logloss: 0.265491\n",
      "[900]\ttraining's multi_logloss: 0.26268\tvalid_1's multi_logloss: 0.264756\n",
      "[1000]\ttraining's multi_logloss: 0.262078\tvalid_1's multi_logloss: 0.264348\n",
      "[1100]\ttraining's multi_logloss: 0.261664\tvalid_1's multi_logloss: 0.264123\n",
      "[1200]\ttraining's multi_logloss: 0.261364\tvalid_1's multi_logloss: 0.263998\n",
      "[1300]\ttraining's multi_logloss: 0.261128\tvalid_1's multi_logloss: 0.263923\n",
      "[1400]\ttraining's multi_logloss: 0.260948\tvalid_1's multi_logloss: 0.263867\n",
      "[1500]\ttraining's multi_logloss: 0.260794\tvalid_1's multi_logloss: 0.26384\n",
      "[1600]\ttraining's multi_logloss: 0.26064\tvalid_1's multi_logloss: 0.263809\n",
      "[1700]\ttraining's multi_logloss: 0.260501\tvalid_1's multi_logloss: 0.263804\n",
      "[1800]\ttraining's multi_logloss: 0.260361\tvalid_1's multi_logloss: 0.263805\n",
      "[1900]\ttraining's multi_logloss: 0.260227\tvalid_1's multi_logloss: 0.263809\n",
      "[2000]\ttraining's multi_logloss: 0.260089\tvalid_1's multi_logloss: 0.263806\n",
      "[2100]\ttraining's multi_logloss: 0.259962\tvalid_1's multi_logloss: 0.263818\n",
      "[2200]\ttraining's multi_logloss: 0.259839\tvalid_1's multi_logloss: 0.263837\n",
      "[2300]\ttraining's multi_logloss: 0.259714\tvalid_1's multi_logloss: 0.263843\n",
      "[2400]\ttraining's multi_logloss: 0.259593\tvalid_1's multi_logloss: 0.263861\n",
      "[2500]\ttraining's multi_logloss: 0.259478\tvalid_1's multi_logloss: 0.26388\n",
      "[2600]\ttraining's multi_logloss: 0.259365\tvalid_1's multi_logloss: 0.263886\n",
      "Early stopping, best iteration is:\n",
      "[1672]\ttraining's multi_logloss: 0.260538\tvalid_1's multi_logloss: 0.263802\n"
     ]
    },
    {
     "data": {
      "image/png": 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Xi4CPgcuBT4pbwGqcoseLmaFi5w5Vy3TppZfywgsv0LNnT+rVqxelxKYiisWJ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\nfZ2lp/7Ni9/xRQnXALPM587It+5xpH9uWvzaHA1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d76a45dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 8 log loss 0.26380198476092676 in 1672\n",
      "Fold :  9\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.534809\tvalid_1's multi_logloss: 0.52501\n",
      "[200]\ttraining's multi_logloss: 0.368056\tvalid_1's multi_logloss: 0.355506\n",
      "[300]\ttraining's multi_logloss: 0.308497\tvalid_1's multi_logloss: 0.295423\n",
      "[400]\ttraining's multi_logloss: 0.284646\tvalid_1's multi_logloss: 0.271715\n",
      "[500]\ttraining's multi_logloss: 0.274176\tvalid_1's multi_logloss: 0.261519\n",
      "[600]\ttraining's multi_logloss: 0.269127\tvalid_1's multi_logloss: 0.256815\n",
      "[700]\ttraining's multi_logloss: 0.266464\tvalid_1's multi_logloss: 0.254484\n",
      "[800]\ttraining's multi_logloss: 0.264926\tvalid_1's multi_logloss: 0.253234\n",
      "[900]\ttraining's multi_logloss: 0.263963\tvalid_1's multi_logloss: 0.252541\n",
      "[1000]\ttraining's multi_logloss: 0.263336\tvalid_1's multi_logloss: 0.252148\n",
      "[1100]\ttraining's multi_logloss: 0.262906\tvalid_1's multi_logloss: 0.251921\n",
      "[1200]\ttraining's multi_logloss: 0.262597\tvalid_1's multi_logloss: 0.251798\n",
      "[1300]\ttraining's multi_logloss: 0.262354\tvalid_1's multi_logloss: 0.251723\n",
      "[1400]\ttraining's multi_logloss: 0.262152\tvalid_1's multi_logloss: 0.251691\n",
      "[1500]\ttraining's multi_logloss: 0.261983\tvalid_1's multi_logloss: 0.251692\n",
      "[1600]\ttraining's multi_logloss: 0.261833\tvalid_1's multi_logloss: 0.251712\n",
      "[1700]\ttraining's multi_logloss: 0.26169\tvalid_1's multi_logloss: 0.251736\n",
      "[1800]\ttraining's multi_logloss: 0.261557\tvalid_1's multi_logloss: 0.251757\n",
      "[1900]\ttraining's multi_logloss: 0.26142\tvalid_1's multi_logloss: 0.251796\n",
      "[2000]\ttraining's multi_logloss: 0.261284\tvalid_1's multi_logloss: 0.251838\n",
      "[2100]\ttraining's multi_logloss: 0.261151\tvalid_1's multi_logloss: 0.251866\n",
      "[2200]\ttraining's multi_logloss: 0.261025\tvalid_1's multi_logloss: 0.251897\n",
      "[2300]\ttraining's multi_logloss: 0.2609\tvalid_1's multi_logloss: 0.251908\n",
      "[2400]\ttraining's multi_logloss: 0.260777\tvalid_1's multi_logloss: 0.251931\n",
      "Early stopping, best iteration is:\n",
      "[1408]\ttraining's multi_logloss: 0.262137\tvalid_1's multi_logloss: 0.251689\n"
     ]
    },
    {
     "data": {
      "image/png": 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EngXuBGr4H5Y0xoQuf0xTIJUrV6Zjx45s2bKlBCszXlTig1Y0xDsFYzFO3hOt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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d76bbc898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 9 log loss 0.25168880960142886 in 1408\n",
      "Training  Finish\n"
     ]
    }
   ],
   "source": [
    "pred, log_loss, oofs = fit_every_feature_model(ensemble_trains, labels, ensemble_tests, fold_count=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word level log_loss 0.26353497323315794\n"
     ]
    }
   ],
   "source": [
    "print(\"Word level log_loss\", log_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oofs = np.concatenate(oofs)\n",
    "oofs.argmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def np_weighted_accuracy(y_true, y_pred):\n",
    "    weight = np.array([[1/16, 1/15, 1/5]])\n",
    "    norm = [(1/16) + (1/15) + (1/5)]\n",
    "    weight_mask = weight * y_true\n",
    "    weight_mask = np.max(weight_mask, axis=-1)\n",
    "    norms = np.sum(weight_mask)\n",
    "    \n",
    "    y_true = np.argmax(y_true, axis=-1)\n",
    "    y_pred = np.argmax(y_pred, axis=-1)\n",
    "    \n",
    "    res = ((y_true == y_pred) * weight_mask).sum() / norms\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "score = np_weighted_accuracy(to_categorical(labels), oofs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score 0.8590955485191333\n",
      "Predicting training results...\n",
      "Predicting labeled testing results...\n"
     ]
    }
   ],
   "source": [
    "print(\"score\", score)\n",
    "oofs_dir = \"../data/ensemble/oofs/\"\n",
    "output_dir = \"../data/ensemble/preds/\"\n",
    "onehot_pred_dir = \"../data/ensemble/nn_one_hot/\"\n",
    "\n",
    "model_submit_prefix = \"LightGBMWordLevel-Ensemble\"\n",
    "\n",
    "oofs_path = oofs_dir + model_submit_prefix\n",
    "output_path = output_dir + model_submit_prefix\n",
    "one_hot_pred_path = onehot_pred_dir + \"One-Hot\" + model_submit_prefix\n",
    "\n",
    "print(\"Predicting training results...\")\n",
    "oofs = pd.DataFrame({\"unrelated\": oofs[:, 0], \"agreed\": oofs[:, 1], \"disagreed\": oofs[:, 2]})\n",
    "submit_path = oofs_path + \"-Train-L{:4f}-NB{:d}.csv\".format(score, NB_WORDS)\n",
    "oofs.to_csv(submit_path, index=False)\n",
    "\n",
    "test_predicts = pd.DataFrame({\"unrelated\": pred[:, 0], \"agreed\": pred[:, 1], \"disagreed\": pred[:, 2]})\n",
    "submit_path = output_path + \"-L{:4f}-NB{:d}.csv\".format(score, NB_WORDS)\n",
    "test_predicts.to_csv(submit_path, index=False) # 0.3343\n",
    "\n",
    "print(\"Predicting labeled testing results...\")\n",
    "ids = pd.read_csv(\"../data/dataset/test.csv\")\n",
    "pred_labels = test_predicts.idxmax(axis=1)\n",
    "sub = pd.DataFrame({\"Id\": ids['id'].values, \"Category\": pred_labels})\n",
    "submit_path = one_hot_pred_path + \"-L{:4f}-NB{:d}.csv\".format(score, NB_WORDS)\n",
    "sub.to_csv(submit_path, index=False)"
   ]
  }
 ],
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